
#importing libraries
import os.path
from gensim import corpora
from gensim.models import LsiModel
from nltk.tokenize import RegexpTokenizer
from nltk.corpus import stopwords
from nltk.stem.porter import PorterStemmer
from gensim.models.coherencemodel import CoherenceModel
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import sklearn as s
import dataframe_image as dfi
import networkx as nx
import mantel

C:\ProgramData\Anaconda3\lib\site-packages\gensim\similarities\__init__.py:15: UserWarning: The gensim.similarities.levenshtein submodule is disabled, because the optional Levenshtein package <https://pypi.org/project/python-Levenshtein/> is unavailable. Install Levenhstein (e.g. `pip install python-Levenshtein`) to suppress this warning.
  warnings.warn(msg)


def load_data(path,file_name,seperator):
    """
    Input  : path and file_name
    function: loading text file
    Output : number of words, document list
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r", encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            i=i+1
            if text !='':
                document = document+text
                if text == seperator:
                    documents_list.append(document)
                    document = ''
        documents_list.append(document)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)


document_list,titles,word_count_science=load_data("","covid-science-small.txt",'NEW_PAPER')

Number of words in text file : 59691
Total Number of Documents: 10


def preprocess_data(doc_set):
    """
    Input  : list of documents
    function: text preprocessing - word tokenize, removing english stopwords, and potter stemming 
    Output : clean text
    """
    # initialize regex tokenizer
    word_tokenizer = RegexpTokenizer(r'\w+')
    # create English stop words list
    eng_stop = set(stopwords.words('english'))
    # Create p_stemmer of class PorterStemmer
    po_stemmer = PorterStemmer()
    # list for tokenized documents in loop
    texts = []
    # loop through document list
    for i in doc_set:
        # clean and tokenize document string
        raw = i.lower()
        tokens = word_tokenizer.tokenize(raw)
        tokens = [i for i in tokens if len(i)>3]
        print(tokens)
        # removing english stop words from tokens
        stopped_tokens = [i for i in tokens if not i in eng_stop]
        # stemmed tokens
        stem_tokens = [po_stemmer.stem(i) for i in stopped_tokens]
        # list of tokens
        texts.append(stem_tokens)
    return texts


clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnetmethodology', 'predicting', 'covid', '19based', 'chest', 'imagess', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herreraabstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'orchest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'mostmedical', 'centers', 'hence', 'many', 'cases', 'imagesbecome', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networkshave', 'great', 'potential', 'building', 'covid', 'triage', 'systemsand', 'detecting', 'covid', 'patients', 'especially', 'patients', 'withlow', 'severity', 'unfortunately', 'current', 'databases', 'allowbuilding', 'such', 'systems', 'they', 'highly', 'heterogeneousand', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'bymost', 'recent', 'covid', 'classification', 'models', 'under', 'aclose', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'sancecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levelsmanuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date', 'publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'bythe', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundaciónbbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '192020', 'spanish', 'ministry', 'science', 'technology', 'under', 'theproject', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 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'would', 'reduce', 'healthcare', 'costs', 'improvediagnosis', 'response', 'time', 'accuracy', 'case', 'volume', '2020', '226825j', 'arias', 'londoño', 'artificial', 'intelligence', 'applied', 'chest', 'images', 'automatic', 'detection', 'covid', '19the', 'deployment', 'these', 'algorithms', 'exempt', 'fromcontroversies', 'hosting', 'models', 'cloud', 'servicewould', 'entail', 'uploading', 'images', 'that', 'might', 'subjectto', 'national', 'international', 'regulations', 'constraints', 'toensure', 'privacy']


# preparing corpus for LSA
def prepare_corpus(doc_clean):
    """
    Input  : preprossed document
    function: creating term dictionary and document term matrix
    Output : term dictionary and Document Term Matrix
    """
    # every unique term is assigned an index. dictionary = corpora.Dictionary(doc_clean)
    term_dictionary = corpora.Dictionary(doc_clean)
    # Converting list of documents (corpus) into Document Term Matrix using dictionary prepared above.
    docu_term_matrix = [term_dictionary.doc2bow(doc) for doc in doc_clean]
    # generate LDA model
    return term_dictionary,docu_term_matrix


def create_gensim_lsa_model(doc_clean,number_of_topics,words):
    """
    Input  : preprocessed document, number of topics and word count
    function: create LSA model using gensim
    Output : return LSA model
    """
    term_dictionary,docu_term_matrix=prepare_corpus(doc_clean)
    # LSA model
    lsamodel = LsiModel(docu_term_matrix, num_topics=number_of_topics, id2word = term_dictionary)  # train model
    return lsamodel


# LSA Model
number_of_topics=10
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_covid_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values


#dictionary of top 5 words
dict(model_science.show_topic(0, topn=5))

{'covid': 0.43749904050277927,
 'model': 0.3134819868536473,
 'imag': 0.30230356131240915,
 'use': 0.2098856921185124,
 'class': 0.1904748020650815}


#wordcloud API
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph6.png')


# Generating Cosine_similarity matrix for top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled6.png')

cosine-similarity
          covid      model      imag       use      class
covid         1   0.242425  0.552974  0.415864   0.726722
model  0.242425          1  -0.24754  0.116798 -0.0427932
imag   0.552974   -0.24754         1  0.484501   0.444943
use    0.415864   0.116798  0.484501         1   0.239286
class  0.726722 -0.0427932  0.444943  0.239286          1


# Generating NetworkX graph for top 5 words from the corpus

df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig6.png')


#loading covid non science dataset
document_list,titles,word_count_nscience=load_data("","covid-non-science-small.txt",'NEW NEWSPAPER')

Number of words in text file : 5231
Total Number of Documents: 10


#data preprocessing
clean_text=preprocess_data(document_list)

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['origins', 'covid', '19by', 'reuterspublished', 'january', '2021', 'updated', 'january', '2021shanghai', 'reuters', 'team', 'from', 'world', 'health', 'organization', 'works', 'china', 'investigate', 'origins', 'covid', 'following', 'factbox', 'looks', 'what', 'know', 'about', 'pandemic', 'began', 'china', 'originsthe', 'coronavirus', 'that', 'causes', 'covid', 'known', 'sars', 'first', 'identified', 'central', 'chinese', 'city', 'wuhan', 'january', '2020', 'most', 'scientists', 'believe', 'still', 'most', 'likely', 'that', 'originated', 'china', 'peter', 'embarek', 'expert', 'food', 'safety', 'zoonotic', 'diseases', 'said', 'wuhan', 'offered', 'first', 'solid', 'clues', 'about', 'transmission', 'covid', 'investigation', 'would', 'start', 'there', 'though', 'some', 'studies', 'have', 'suggested', 'covid', 'present', 'italy', 'spain', 'earlier', '2019', 'that', 'might', 'have', 'been', 'responsible', 'spike', 'pneumonia', 'cases', 'france', 'researchers', 'believe', 'could', 'have', 'entered', 'wuhan', 'from', 'europe', 'closest', 'relative', 'nature', 'ratg13', 'virus', 'which', 'discovered', 'horseshoe', 'bats', 'southwest', 'china', 'yunnan', 'province', 'genetic', 'match', 'between', 'makes', 'highly', 'likely', 'that', 'covid', 'also', 'originated', 'colonies', 'china', 'southwest', 'border', 'regions', 'huanan', 'marketthe', 'initial', 'cluster', 'infections', 'traced', 'back', 'huanan', 'seafood', 'market', 'wuhan', 'leading', 'many', 'assume', 'that', 'patient', 'zero', 'probably', 'trader', 'exposed', 'contaminated', 'meat', 'products', 'this', 'version', 'events', 'simplistic', 'explain', 'patterns', 'infection', 'wuhan', 'elsewhere', 'many', 'earliest', 'reported', 'cases', 'connection', 'with', 'market', '2019', 'wuhan', 'residents', 'were', 'hospitalised', 'with', 'what', 'turned', 'covid', 'them', 'link', 'huanan', 'team', 'chinese', 'researchers', 'said', 'there', 'were', 'types', 'sars', 'circulating', 'wuhan', 'only', 'which', 'associated', 'with', 'market', 'scientists', 'also', 'said', 'virus', 'unusually', 'adapted', 'rapid', 'human', 'transmission', 'making', 'unlikely', 'that', 'first', 'human', 'contact', 'made', 'seafood', 'market', 'intermediary', 'species', 'investigators', 'keen', 'find', 'what', 'intermediary', 'species', 'that', 'allowed', 'sars', 'move', 'from', 'original', 'host', 'into', 'humans', 'preliminary', 'scientific', 'papers', 'china', 'identified', 'snakes', 'mink', 'potential', 'candidates', 'similar', 'coronavirus', 'infections', 'were', 'also', 'found', 'pangolins', 'illegally', 'trafficked', 'into', 'china', 'some', 'scholars', 'believe', 'there', 'intermediary', 'species', 'that', 'sars', 'like', 'virus', 'transmitted', 'directly', 'from', 'bats', 'humans', 'possibly', 'multiple', 'occasions', 'first', 'people', 'infected', 'were', 'likely', 'traders', 'meat', 'droppings', 'used', 'traditional', 'chinese', 'medicine', 'them', 'could', 'have', 'carried', 'into', 'huanan', 'seafood', 'market', 'causing', 'superspreader', 'event', 'that', 'allowed', 'pandemic', 'begin', 'gain', 'function', 'though', 'there', 'credible', 'supporting', 'evidence', 'some', 'researchers', 'still', 'rule', 'possibility', 'that', 'virus', 'released', 'accidentally', 'specialist', 'wuhan', 'institute', 'virology', 'there', 'indication', 'that', 'sars', 'contains', 'synthetic', 'insertions', 'some', 'researchers', 'could', 'have', 'been', 'subject', 'process', 'known', 'gain', 'function', 'where', 'forces', 'virus', 'become', 'more', 'infectious', 'exposing', 'human', 'receptor', 'cells', 'some', 'experts', 'investigators', 'needs', 'granted', 'access', 'research', 'conducted', 'whether', 'there', 'closer', 'matches', 'than', 'ratg13', 'virus', 'there', 'another', 'explanation', 'coronavirus', 'proven', 'lethal', 'sars', 'been', 'circulating', 'humans', 'throughout', 'southwest', 'china', 'months', 'before', 'finally', 'identified', 'wuhan', 'natural', 'selection', 'itself', 'could', 'have', 'trained', 'bind', 'more', 'effectively', 'those', 'receptor', 'cells', 'fully', 'optimised', 'after', 'months', 'human', 'exposure', 'made', 'explosive', 'breakthrough', 'huanan', 'market', 'where', 'conditions', 'were', 'ideal', 'rapid', 'viral', 'transmission', 'though', 'also', 'very', 'likely', 'spreading', 'elsewhere', 'reporting', 'david', 'stanway', 'editing', 'gerry', 'doyle', 'newspaper']
['australian', 'covid', 'testing', 'criteriaby', 'australian', 'associated', 'presspublished', 'april', '2020', 'updated', 'april', '2020what', 'criteria', 'people', 'australia', 'need', 'meet', 'order', 'tested', 'covid', 'victoria', 'testing', 'expanded', 'victoria', 'include', 'anyone', 'experiencing', 'fever', 'acute', 'respiratory', 'symptoms', 'testing', 'available', 'anyone', 'recently', 'returned', 'from', 'travel', 'come', 'into', 'contact', 'with', 'confirmed', 'case', 'public', 'screening', 'clinics', 'have', 'opened', 'test', 'people', 'covid', 'south', 'wales', 'testing', 'recommended', 'anyone', 'displaying', 'fever', 'acute', 'respiratory', 'infection', 'symptoms', 'overseas', 'travellers', 'past', 'days', 'cruise', 'ship', 'passengers', 'crew', 'members', 'those', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'case', 'urged', 'tested', 'sick', 'healthcare', 'aged', 'care', 'workers', 'recommended', 'tested', 'infection', 'people', 'living', 'penrith', 'sydney', 'inner', 'west', 'liverpool', 'randwick', 'waverley', 'woollahra', 'blacktown', 'cumberland', 'westmead', 'ryde', 'manning', 'lake', 'macquarie', 'urged', 'seek', 'testing', 'they', 'display', 'symptoms', 'community', 'community', 'transmission', 'occurred', 'these', 'areas', 'queensland', 'people', 'displaying', 'fever', 'acute', 'respiratory', 'symptoms', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'case', 'have', 'been', 'overseas', 'within', 'past', 'days', 'tested', 'workers', 'vulnerable', 'setting', 'including', 'healthcare', 'aged', 'care', 'military', 'education', 'corrections', 'tested', 'people', 'live', 'brisbane', 'gold', 'coast', 'cairns', 'first', 'nations', 'community', 'also', 'tested', 'they', 'show', 'symptoms', 'people', 'travelling', 'from', 'declared', 'covid', 'hotspot', 'interstate', 'also', 'tested', 'south', 'australia', 'residents', 'advised', 'tested', 'they', 'have', 'travelled', 'overseas', 'interstate', 'past', 'days', 'testing', 'advised', 'people', 'have', 'been', 'contact', 'with', 'confirmed', 'case', 'displaying', 'coronavirus', 'symptoms', 'healthcare', 'workers', 'with', 'direct', 'patient', 'contact', 'aged', 'residential', 'care', 'workers', 'have', 'fever', 'acute', 'respiratory', 'infection', 'shortness', 'breath', 'cough', 'sore', 'throat', 'urged', 'tested', 'people', 'tanunda', 'nuriootpa', 'williamstown', 'angaston', 'lyndoch', 'from', 'march', 'have', 'developed', 'covid', 'symptoms', 'should', 'immediately', 'self', 'isolate', 'tested', 'after', 'cases', 'were', 'linked', 'barossa', 'region', 'health', 'recommends', 'testing', 'qantas', 'staff', 'have', 'been', 'affected', 'areas', 'adelaide', 'airport', 'after', 'outbreak', 'cases', 'public', 'areas', 'have', 'been', 'affected', 'anyone', 'been', 'airport', 'developed', 'symptoms', 'urged', 'self', 'isolate', 'seek', 'testing', 'western', 'australia', 'people', 'tested', 'they', 'have', 'fever', 'recent', 'history', 'fever', 'acute', 'respiratory', 'infection', 'shortness', 'breath', 'cough', 'sore', 'throat', 'people', 'working', 'high', 'risk', 'settings', 'such', 'healthcare', 'aged', 'care', 'disability', 'police', 'officers', 'tested', 'residents', 'required', 'have', 'travelled', 'interstate', 'overseas', 'been', 'contact', 'with', 'confirmed', 'case', 'test', 'people', 'metropolitan', 'areas', 'tested', 'nine', 'testing', 'clinics', 'while', 'those', 'regional', 'areas', 'tested', 'public', 'hospital', 'health', 'service', 'remote', 'health', 'clinic', 'authorities', 'indicate', 'more', 'random', 'testing', 'find', 'symptomatic', 'carriers', 'likely', 'next', 'step', 'after', 'very', 'little', 'community', 'spread', 'found', 'state', 'results', 'tasmania', 'tasmanians', 'display', 'coronavirus', 'symptoms', 'have', 'recently', 'travelled', 'overseas', 'interstate', 'apply', 'their', 'through', 'public', 'health', 'services', 'tested', 'people', 'have', 'been', 'cruise', 'ship', 'have', 'been', 'contact', 'with', 'person', 'with', 'covid', 'apply', 'tested', 'state', 'government', 'changed', 'testing', 'criteria', 'allow', 'testing', 'people', 'have', 'spent', 'time', 'tasmania', 'northwest', 'have', 'developed', 'fever', 'respiratory', 'symptoms', 'northern', 'territory', 'people', 'have', 'returned', 'from', 'interstate', 'overseas', 'past', 'days', 'develop', 'respiratory', 'illness', 'with', 'without', 'fever', 'tested', 'people', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'covid', 'case', 'past', 'days', 'develop', 'respiratory', 'illness', 'with', 'without', 'fever', 'tested', 'people', 'have', 'severe', 'community', 'acquired', 'pneumonia', 'there', 'clear', 'cause', 'seek', 'testing', 'healthcare', 'workers', 'directly', 'work', 'with', 'patients', 'frontline', 'workers', 'including', 'police', 'emergency', 'workers', 'educators', 'retail', 'pharmacists', 'disability', 'workers', 'display', 'respiratory', 'illness', 'fever', 'tested', 'people', 'have', 'recently', 'travelled', 'overseas', 'cruise', 'ship', 'have', 'been', 'contact', 'with', 'confirmed', 'case', 'have', 'developed', 'symptoms', 'covid', 'within', 'days', 'returning', 'australia', 'urged', 'tested', 'healthcare', 'aged', 'care', 'workers', 'with', 'recent', 'onset', 'respiratory', 'symptoms', 'fever', 'irrespective', 'travel', 'history', 'tested', 'people', 'living', 'high', 'risk', 'setting', 'such', 'aged', 'care', 'military', 'correctional', 'facilities', 'have', 'symptoms', 'covid', 'should', 'tested', 'anyone', 'travelled', 'from', 'area', 'where', 'covid', 'outbreaks', 'have', 'occurred', 'have', 'travelled', 'area', 'within', 'australia', 'with', 'elevated', 'risk', 'community', 'transmission', 'displaying', 'symptoms', 'urged', 'tested', 'newspaper']
['auckland', 'removes', 'covid', 'restrictionsby', 'australian', 'associated', 'presspublished', 'march', '2021', 'updated', 'march', '2021new', 'zealand', 'downscaled', 'covid', 'alert', 'levels', 'auckland', 'marking', 'restrictions', 'from', 'valentine', 'outbreak', 'however', 'jacinda', 'ardern', 'government', 'been', 'criticised', 'making', 'decision', 'thursday', 'delaying', 'announcement', 'first', 'bloke', 'clarke', 'gayford', 'jumped', 'prime', 'minister', 'tweeting', 'government', 'decision', 'four', 'hours', 'before', 'ardern', 'scheduled', 'press', 'conference', 'inprinciple', 'decision', 'decided', 'pending', 'final', 'test', 'results', 'today', 'wrote', 'response', 'criticism', 'from', 'kiwi', 'chef', 'martin', 'bosley', 'feeling', 'really', 'sorry', 'auckland', 'hospo', 'having', 'wait', 'until', '30am', 'today', 'find', 'about', 'moving', 'levels', 'utterly', 'ludicrous', 'stay', 'strong', 'friends', 'bosley', 'said', 'aucklanders', 'were', 'expecting', 'shift', 'there', 'have', 'been', 'community', 'cases', 'fortnight', '30am', 'ardern', 'announced', 'biggest', 'city', 'would', 'move', 'from', 'alert', 'level', 'alert', 'level', '12noon', 'once', 'again', 'city', 'stepped', 'what', 'needed', 'that', 'thank', 'said', 'look', 'after', 'each', 'other', 'enjoy', 'your', 'weekend', 'decision', 'allows', 'crowds', 'gather', 'watch', 'america', 'sailing', 'auckland', 'racing', 'meet', 'saturday', 'postponed', 'auckland', 'pride', 'events', 'business', 'hospitality', 'figures', 'attacked', 'decision', 'saying', 'allow', 'them', 'enough', 'time', 'change', 'staffing', 'order', 'supplies', 'bumper', 'friday', 'night', 'trade', 'auckland', 'pride', 'director', 'tweedie', 'said', 'wait', 'frustrating', 'sector', 'anticipated', 'move', 'tonight', 'which', 'changes', 'everything', 'friday', 'night', 'shows', 'events', 'said', 'need', 'time', 'roll', 'plans', 'release', 'tickets', 'change', 'seating', 'signage', 'didn', 'opposition', 'leader', 'judith', 'collins', 'called', 'communications', 'good', 'enough', 'ardern', 'said', 'cabinet', 'thursday', 'afternoon', 'decided', 'move', 'alert', 'levels', 'pending', 'cases', 'testing', 'close', 'contacts', 'information', 'that', 'friday', 'morning', 'ardern', 'rejected', 'notion', 'gayford', 'tweet', 'improper', 'have', 'seen', 'partner', 'since', 'yesterday', 'really', 'reject', 'implication', 'said', 'known', 'decision', 'known', 'process', 'which', 'exactly', 'what', 'ministers', 'have', 'conveyed', 'public', 'domain', 'valentine', 'outbreak', 'first', 'identified', 'february', 'prompted', 'government', 'into', 'lockdowns', 'zealand', 'biggest', 'city', 'first', 'three', 'lockdown', 'beginning', 'february', 'week', 'long', 'lockdown', 'began', 'february', 'when', 'community', 'case', 'identified', 'without', 'links', 'cluster', 'subsequently', 'traced', 'back', 'outbreak', 'total', 'people', 'have', 'tested', 'positive', 'covid', 'from', 'latest', 'outbreak', 'health', 'officials', 'discover', 'origin']


# LSA Model
number_of_topics=10
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))#dict and encoding matrix values


# generated top 5 words using lSA
dict(model_nscience.show_topic(0,topn=5))

{'test': 0.5996895875834207,
 'peopl': 0.2882010064110186,
 'covid': 0.23768902176777906,
 'symptom': 0.21329725997683804,
 'case': 0.19112113436138517}


#wordcloud API
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph7.png')


# Cosine similarity matrix for top 5 words from corpus

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled7.png')

cosine-similarity
             test     peopl      covid    symptom      case
test            1  0.833309   0.286923   0.911199  0.356577
peopl    0.833309         1   0.226664   0.919123   0.19254
covid    0.286923  0.226664          1  0.0492026  0.370479
symptom  0.911199  0.919123  0.0492026          1  0.273434
case     0.356577   0.19254   0.370479   0.273434         1


#Generated Networkx for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="y")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig7.png')


#loading covid psychology small dataset
document_list,titles,word_count_psy=load_data("","covid-psychology-SMALL.txt",'NEW PAPER')

Number of words in text file : 39735
Total Number of Documents: 10


#data preprocessing/cleaning
clean_text=preprocess_data(document_list)

['international', 'sociology2021', 'author', '2020article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissionsdoi', '1177', '0268580920948807journals', 'sagepub', 'home', 'isspsychology', 'politics', 'ofcovid', 'misinfodemics', 'peoplebelieve', 'misinfodemics', 'sonia', 'mukhtaruniversity', 'management', 'technology', 'lahore', 'pakistanabstractmisinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adversehealth', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicateevidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'ofmisinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevierabout', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'thisreview', 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'squared', '912909adjusted', 'squared', '892007durbin', 'watson', 'stat', '006439variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob', 'usc_pos', '004491', '001667', '694157', '0099usc_neg', '054381', '014406', '774912', '0005usd_pos', '064436', '029631', '174626', '035usd_neg', '290139', '338048', '858278', '3953c', '181416', '264851', '684971', '4969', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'table', 'asymmetric', 'parameters', 'variables', 'short', 'term', 'coefficients', 'error', 'stat', 'prob', 'usd_neg', '039324', '019503', '016267', '0487d', 'usc_pos', '002147', '000382', '622014', 'usc_neg', '002319', '00069', '360414', '0014d', 'usc_neg', '008198', '001064', '703282', '0ectt', '501702', '093855', '00026', 'squared', '812596adjusted', 'squared', '800102durbin', 'watson', 'stat', '130245variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob', 'usd_pos', '013042', '004465', '920922', '0051usd_neg', '026413', '030567', '864107', '3913usc_pos', '001013', '00034', '977381', '0043usc_neg', '004258', '001923', '214416', '031c', '525513', '274541', '914153', '0608', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'that', 'rise', 'reduction', 'itld', 'diminished', 'amplified', 'ftmr', 'inferring', 'that', 'heath', 'crisis', 'imperatively', 'contributes', 'italy', 'sfinancial', 'instability', 'long', 'term', 'period', 'addition', 'growthin', 'itlc', 'enlarged', 'ftmr', 'imperatively', 'sort', 'conditionin', 'italy', 'denoted', 'that', 'first', 'second', 'third', 'values', 'ftmrharmed', 'returns', 'also', 'growth', 'itlc', 'negativeimpression', 'ftmr', 'nonetheless', 'negative', 'shock', 'thesecond', 'value', 'itlc', 'indicated', 'encouraging', 'effect', 'onftmr', 'consequences', 'itld', 'symbolized', 'that', 'proliferationin', 'itld', 'possess', 'negative', 'sway', 'ftmer', 'first', 'andsecond', 'value', 'have', 'definite', 'sway', 'furthermore', 'declinein', 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'154368variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob', 'spd_pos', '053991', '01765', '0589', '0034spd_neg', '023488', '018017', '3037', '1978spc_pos', '006341', '001968', '0021spc_neg', '005173', '001945', '0102c', '636028', '428132', '1431', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'table', 'asymmetric', 'parameters', 'ftmr', 'variables', 'short', 'term', 'coefficients', 'error', 'stat', 'prob', 'ftmr', '27947', '138377', '019624', '0493d', 'ftmr', '154875', '136743', '132605', '2632d', 'ftmr', '295127', '113398', '602569', '0124d', 'itlc_pos', '008711', '001397', '235569', 'itlc_pos', '015154', '003264', '642728', 'itlc_pos', '0047', '002589', '573306', '1225d', 'itlc_neg', '001115', '001937', '575378', '5678d', 'itlc_neg', '006885', '00198', '477965', '0011d', 'itlc_neg', '003758', '001782', '109409', '0404d', 'itld_pos', '06983', '014714', '745672', 'itld_pos', '023801', '011962', '989722', '0526d', 'itld_pos', '029575', '010511', '813763', '0072d', 'itld_neg', '002565', '012862', '199404', '8428d', 'itld_neg', '038093', '015194', '507186', '0158d', 'itld_neg', '04502', '013664', '294772', '0019ectt', '959373', '163657', '862111', 'squared', '851016adjusted', 'squared', '807197durbin', 'watson', 'stat', '763297variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob', 'itlc_pos', '013317', '005649', '357295', '0227itlc_neg', '001321', '002682', '492464', '6247itld_pos', '109433', '053273', '054209', '0457itld_neg', '048345', '020559', '351573', '023c', '500777', '455916', '098396', '2777', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'parameters', 'table', 'directed', 'thelong', 'upsurge', 'diminution', 'gerd', 'compacted', 'enlarge', 'daxr', 'moreover', 'decline', 'gerc', 'alsoimproves', 'daxr', 'these', 'findings', 'revealed', 'that', 'both', 'lockdownand', 'health', 'crises', 'harmful', 'stock', 'markets', 'ofgermany', 'short', 'term', 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'850055', '56576', 'gerd_neg', '76795', '466544', '20677', 'gerd_neg', '4969', '21192', '08761', '0ectt', '497359', '029762', '71134', 'squared', '945788adjusted', 'squared', '910937durbin', 'watson', 'stat', '667758variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob', 'gerc_pos', '016748', '035958', '465776', '6458gerc_neg', '255912', '1228', '083972', '0485gerd_pos', '28734', '569148', '548151', '018gerd_neg', '49979', '3728', '581496', '0167c', '440744', '93232', '617925', '0154', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'table', 'asymmetric', 'parameters', 'lser', 'variables', 'short', 'term', 'coefficients', 'error', 'stat', 'prob', 'ukc_pos', '00127', '001661', '764723', '4475d', 'ukc_pos', '022345', '003248', '878603', 'ukc_neg', '009267', '002137', '337228', '0001d', 'ukd_pos', '03503', '00746', '695883', 'ukd_pos', '090212', '014812', '090388', '0ectt', '100754', '115104', '563147', 'squared', '651816adjusted', 'squared', '624182durbin', 'watson', 'stat', '867108variables', 'long', 'term', 'coefficients', 'error', 'stat', 'prob', 'ukc_pos', '003609', '00191', '889866', '0638ukc_neg', '003213', '002112', '521044', '1337ukd_pos', '075038', '02864', '620078', '0112ukd_neg', '086022', '035056', '453823', '0172c', '319529', '353596', '903654', '3699', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'third', 'fourth', 'positive', 'shocks', 'gerc', 'alessening', 'impact', 'daxr', 'however', 'negative', 'shock', 'ingerc', 'other', 'values', 'except', 'first', 'indicatedan', 'indirect', 'association', 'with', 'daxr', 'additionally', 'positiveshocks', 'gerd', 'quantified', 'mixed', 'impact', 'daxr', 'while', 'short', 'term', 'decline', 'gerd', 'negative', 'firstfrontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time', '11shehzad', 'financial', 'markets', 'behavior', 'covid', '19lag', 'second', 'third', 'values', 'point', 'positivelinkage', 'with', 'daxr', 'these', 'effects', 'because', 'instabilitygenerated', 'covid', 'squared', 'coefficientdiagnosed', 'that', 'instability', 'daxr', 'becauseof', 'covid', 'table', 'represents', 'results', 'london', 'stockexchange', 'concluding', 'coefficients', 'explored', 'thataugmentation', 'improved', 'lser', 'while', 'diminutionin', 'reduces', 'lser', 'long', 'term', 'period', 'althoughthe', 'growth', 'brought', 'significant', 'decrease', 'lser', 'short', 'term', 'period', 'outlined', 'that', 'positive', 'shocks', 'ukcreduced', 'lser', 'first', 'value', 'positive', 'shockin', 'showed', 'alternate', 'impression', 'additionally', 'anupturn', 'possesses', 'diminishing', 'impact', 'lser', 'these', 'outcomes', 'designated', 'that', 'lockdown', 'health', 'crisesgenerated', 'covid', 'negatively', 'impact', 'financialmarkets', 'short', 'term', 'period', 'value', 'ofectt', 'showed', 'that', 'fluxes', 'occurred', 'today', 'lser', 'will', 'getequilibrium', 'with', 'speed', 'units', 'next', 'square', 'value', 'reported', 'variation', 'lser', 'dueto', 'covid', 'figure', 'depicts', 'adjustment', 'asymmetriceffect', 'existing', 'long', 'equilibrium', 'once', 'moved', 'anew', 'long', 'equilibrium', 'result', 'positive', 'adverseshocks', 'asymmetric', 'plots', 'denote', 'alliance', 'dynamicmultipliers', 'owing', 'positive', 'adverse', 'shocks', 'ftse', 'nasdaq', 'composite', 'index', 'ibex', 'lsemarkets', 'outcomes', 'revealed', 'that', 'these', 'markets', 'tremendouslyrespond', 'positive', 'negative', 'shocks', 'befallen', 'dueto', 'covid', 'diagnostic', 'parameters', 'evaluationthe', 'examination', 'employed', 'breusch', 'pagan', '2006', 'testto', 'diagnose', 'serial', 'correlation', 'heteroskedasticity', 'ineach', 'model', 'residuals', 'ramsey', 'reset', 'technique', 'ascertainfunctional', 'misspecification', 'cusum', 'cusumsq', 'plobergerand', 'kramer', '2006', 'test', 'define', 'reliability', 'parameters', 'conclusions', 'these', 'tests', 'given', 'table', 'nominatedthat', 'there', 'serial', 'correlation', 'heteroskedasticity', 'inthe', 'residuals', 'each', 'model', 'besides', 'plots', 'cusum', 'andcusumsq', 'presented', 'figure', 'remain', 'within', 'criticalboundaries', 'inferring', 'that', 'coefficients', 'nardl', 'modelare', 'stable', 'conclusioncovid', 'imperatively', 'shaken', 'economic', 'indicators', 'primarily', 'financial', 'markets', 'globe', 'number', 'ofpatients', 'deaths', 'result', 'covid', 'increasingday', 'economic', 'conditions', 'have', 'become', 'entirelyuncertain', 'moreover', 'financial', 'markets', 'world', 'fronteda', 'sudden', 'crash', 'their', 'market', 'values', 'this', 'investigationutilized', 'linear', 'autoregressive', 'distributed', 'nardl', 'approach', 'estimate', 'linear', 'impact', 'economic', 'crisisand', 'health', 'crisis', 'generated', 'result', 'covid', '19pandemic', 'behavior', 'financial', 'markets', 'mostinfected', 'territories', 'europe', 'analysis', 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'serial', 'correlation', 'valuesr', 'ftmrf', 'statistic', 'statistic', '5464', 'statistic', 'statistic', '3644ramsey', 'reset', 'statistic', '3623', 'ramsey', 'reset', 'statistic', '7672nsr', 'daxrheteroskedasticity', 'serial', 'correlation', 'heteroskedasticity', 'serial', 'correlationf', 'statistic', 'statistic', '6362', 'statistic', 'statistic', '8397ramsey', 'reset', 'statistic', '1927', 'ramsey', 'reset', 'statistic', '2731iber', 'lserheteroskedasticity', 'serial', 'correlation', 'heteroskedasticity', 'serial', 'correlationf', 'statistic', 'statistic', '4019', 'statistic', 'statistic', '9939ramsey', 'reset', 'statistic', '4698', 'ramsey', 'reset', 'statistic', '3502', 'denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation', 'figure', 'cusum', 'cusumsq', 'graph', 'health', 'crisis', 'that', 'resulted', 'spain', 'long', 'italy', 'sstock', 'markets', 'more', 'affected', 'health', 'crisis', 'than', 'theeconomic', 'crisis', 'while', 'short', 'economic', 'uncertaintyalso', 'lowered', 'stock', 'returns', 'ftse', 'nevertheless', 'thehealth', 'crisis', 'germany', 'pointing', 'significant', 'upsurge', 'inthe', 'economic', 'crisis', 'intimated', 'unimportant', 'impression', 'theftse', 'index', 'stock', 'markets', 'recorded', 'that', 'short', 'upsurge', 'health', 'crisis', 'imperativelydamages', 'stock', 'returns', 'index', 'thelong', 'this', 'influence', 'left', 'reverse', 'therefore', 'thisresearch', 'authenticated', 'that', 'health', 'crisis', 'begun', 'with', 'covid', 'devised', 'another', 'global', 'financial', 'crisis', 'almostevery', 'nation', 'fighting', 'with', 'these', 'crises', 'today', 'moreover', 'study', 'disclosed', 'that', 'economic', 'uncertainty', 'generated', 'bycovid', 'highest', 'german', 'stock', 'markets', 'whilethe', 'index', 'second', 'ranking', 'italianstock', 'markets', 'third', 'ranking', 'study', 'concludedthat', 'health', 'crisis', 'economic', 'crisis', 'ominously', 'affectedthe', 'stock', 'markets', 'globe', 'consequently', 'significantamount', 'should', 'allocated', 'budget', 'explore', 'andprevent', 'these', 'pandemics', 'future', 'further', 'completelockdown', 'strategy', 'does', 'prove', 'excellent', 'remedyas', 'harms', 'financial', 'markets', 'other', 'strategies', 'should', 'bedeveloped', 'smart', 'partial', 'lockdown', 'these', 'suggestions', 'areessential', 'policymakers', 'government', 'officials', 'researchers', 'andthe', 'general', 'public', 'frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time', '13shehzad', 'financial', 'markets', 'behavior', 'covid', '19data', 'availability', 'statementpublicly', 'available', 'datasets', 'were', 'analyzed', 'this', 'study', 'this', 'datacan', 'found', 'here', 'https', 'ecdc', 'europa', 'author', 'contributionsks', 'conceptualization', 'analysis', 'methodology', 'supervision', 'introduction', 'results', 'discussion', 'andconclusion', 'revising', 'work', 'proofreading', 'visualization', 'authors', 'contributed', 'article', 'approved', 'thesubmitted', 'version']


# LSA Model
number_of_topics=10
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))#dict and encoding matrix values


#top 5 words generated from psychology dataset
dict(model_psy.show_topic(0,topn=5))

{'2020': 0.4097549480231352,
 'covid': 0.37354491310215926,
 'psycholog': 0.2500233429332007,
 'pandem': 0.21749252372444458,
 'impact': 0.1958265527003977}


#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph27.png')


# Cosine similarity matrix for top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled8.png')

cosine-similarity
               2020     covid psycholog    pandem    impact
2020              1  0.553352   0.15666  0.725075  0.507801
covid      0.553352         1  0.538492  0.428987  0.545456
psycholog   0.15666  0.538492         1  0.418044  0.297771
pandem     0.725075  0.428987  0.418044         1  0.645128
impact     0.507801  0.545456  0.297771  0.645128         1


#generating networkx graph for top 5 words 
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig8.png')


# defining mantel test function
def mantel_test(n):
    #all words from corpus
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_psy = dict(model_psy.show_topic(0, topn=word_count_psy))
    
    #top words from the corpus
    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_psy = dict(model_psy.show_topic(0,topn=n)) 

    #Finding out the weights of top 5 law words in covid science and updating their weights w.r.t psychology dataset 
    c={} 
    for key in top_words_science:
        try:
            c[key] = words_psy[key]
        except KeyError:
            c[key] = 0

    top_words_psy.update(c)

    #Finding out the weights of top 5 law words in covid psychology and updating their weights w.r.t covid science dataset 
    b={} 
    for key in top_words_psy:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance for top 10 words in covid science
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace of top 10 words in psychology dataset
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_psy =dict(model_psy.show_topic(i, topn=word_count_psy))
        b=pd.DataFrame(words_from_psy,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_psy.keys()), 
                      index = list(top_words_psy.keys()))

    l = list(top_words_psy.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_psy), k = 1)])

    return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')


#mantel test with 10 word network
mantel_test(5)

Pairwise-distance
               2020     covid    psycholog    pandem     impact     model  \
2020              0  0.534783     0.305609  0.363047   0.310612  0.635014   
covid      0.534783         0     0.483042  0.494405   0.463392  0.703598   
psycholog  0.305609  0.483042  8.23181e-11  0.161631  0.0565689  0.642125   
pandem     0.363047  0.494405     0.161631         0   0.186003  0.646317   
impact     0.310612  0.463392    0.0565689  0.186003          0  0.649139   
model      0.635014  0.703598     0.642125  0.646317   0.649139         0   
imag       0.658395  0.487238     0.541685  0.587713   0.545382  0.936326   
use        0.321917  0.464427     0.349711  0.377897   0.349062  0.694206   
class      0.447607  0.333658     0.383029  0.462755   0.367729  0.761075   

               imag          use     class  
2020       0.658395     0.321917  0.447607  
covid      0.487238     0.464427  0.333658  
psycholog  0.541685     0.349711  0.383029  
pandem     0.587713     0.377897  0.462755  
impact     0.545382     0.349062  0.367729  
model      0.936326     0.694206  0.761075  
imag              0     0.481729  0.505198  
use        0.481729  5.26836e-09  0.452952  
class      0.505198     0.452952         0  
Pairwise-distance
               2020     covid psycholog    pandem    impact      model  \
2020              0  0.505063  0.672686  0.432156  0.512852    0.57884   
covid      0.505063         0  0.407881  0.401712  0.361303   0.400188   
psycholog  0.672686  0.407881         0  0.400096   0.42986   0.433394   
pandem     0.432156  0.401712  0.400096         0   0.24077   0.304339   
impact     0.512852  0.361303   0.42986   0.24077         0   0.260406   
model       0.57884  0.400188  0.433394  0.304339  0.260406          0   
imag       0.595126  0.425849  0.426358  0.291066  0.283301  0.0781712   
use        0.633227  0.417661  0.295343  0.309249  0.316602   0.177739   
class      0.598411  0.425174  0.424199   0.29679  0.281428  0.0757704   

                imag          use        class  
2020        0.595126     0.633227     0.598411  
covid       0.425849     0.417661     0.425174  
psycholog   0.426358     0.295343     0.424199  
pandem      0.291066     0.309249      0.29679  
impact      0.283301     0.316602     0.281428  
model      0.0781712     0.177739    0.0757704  
imag               0     0.158667    0.0402415  
use         0.158667  2.63418e-09     0.154272  
class      0.0402415     0.154272  3.29272e-10

(-0.3228753417554325, 0.88872, -1.1434671968358807)


#mantel test with 20 word network
mantel_test(10)

Pairwise-distance
               2020     covid    psycholog    pandem     impact     health  \
2020              0  0.534783     0.305609  0.363047   0.310612   0.308508   
covid      0.534783         0     0.483042  0.494405   0.463392   0.455973   
psycholog  0.305609  0.483042  8.23181e-11  0.161631  0.0565689  0.0726313   
pandem     0.363047  0.494405     0.161631         0   0.186003   0.147486   
impact     0.310612  0.463392    0.0565689  0.186003          0  0.0614011   
health     0.308508  0.455973    0.0726313  0.147486  0.0614011          0   
market     0.298177  0.484587    0.0140432  0.160027  0.0635518  0.0768204   
econom     0.308531  0.479701    0.0157494  0.151438  0.0621237  0.0696727   
global     0.333443  0.486064    0.0770943  0.110073   0.111356   0.085622   
sustain    0.307778   0.48355    0.0104329  0.153619  0.0628686  0.0733919   
model      0.635014  0.703598     0.642125  0.646317   0.649139   0.637645   
imag       0.658395  0.487238     0.541685  0.587713   0.545382   0.555834   
use        0.321917  0.464427     0.349711  0.377897   0.349062   0.335448   
class      0.447607  0.333658     0.383029  0.462755   0.367729   0.381243   
data       0.480258  0.444024     0.390212  0.342444   0.389558   0.387973   
dataset    0.331684  0.323608     0.271015  0.326681   0.266218   0.264145   
learn      0.577585  0.585678     0.484239  0.491685   0.444625   0.427751   
detect     0.430859  0.482478     0.289782  0.368138   0.295171   0.318517   
perform    0.361485  0.540009      0.31652  0.358585   0.334573   0.311642   

              market       econom     global    sustain     model      imag  \
2020        0.298177     0.308531   0.333443   0.307778  0.635014  0.658395   
covid       0.484587     0.479701   0.486064    0.48355  0.703598  0.487238   
psycholog  0.0140432    0.0157494  0.0770943  0.0104329  0.642125  0.541685   
pandem      0.160027     0.151438   0.110073   0.153619  0.646317  0.587713   
impact     0.0635518    0.0621237   0.111356  0.0628686  0.649139  0.545382   
health     0.0768204    0.0696727   0.085622  0.0733919  0.637645  0.555834   
market             0    0.0213546  0.0773713  0.0162195  0.643042   0.53992   
econom     0.0213546  2.32831e-10  0.0702706  0.0125576  0.642047  0.545831   
global     0.0773713    0.0702706          0  0.0683874  0.631719  0.549419   
sustain    0.0162195    0.0125576  0.0683874          0   0.64075  0.542939   
model       0.643042     0.642047   0.631719    0.64075         0  0.936326   
imag         0.53992     0.545831   0.549419   0.542939  0.936326         0   
use          0.34212     0.353776   0.346131   0.351068  0.694206  0.481729   
class       0.385564     0.381112   0.402811   0.384581  0.761075  0.505198   
data        0.392224     0.385228    0.38517   0.385959  0.617515  0.663423   
dataset     0.271098      0.26479   0.292896   0.271927  0.686945  0.527931   
learn       0.488965     0.488098    0.47507   0.485932  0.771095  0.736457   
detect      0.294642     0.297331   0.324151   0.292679  0.606783  0.493486   
perform      0.31498     0.319565   0.306806   0.316264  0.466261  0.628683   

                   use     class      data   dataset     learn       detect  \
2020          0.321917  0.447607  0.480258  0.331684  0.577585     0.430859   
covid         0.464427  0.333658  0.444024  0.323608  0.585678     0.482478   
psycholog     0.349711  0.383029  0.390212  0.271015  0.484239     0.289782   
pandem        0.377897  0.462755  0.342444  0.326681  0.491685     0.368138   
impact        0.349062  0.367729  0.389558  0.266218  0.444625     0.295171   
health        0.335448  0.381243  0.387973  0.264145  0.427751     0.318517   
market         0.34212  0.385564  0.392224  0.271098  0.488965     0.294642   
econom        0.353776  0.381112  0.385228   0.26479  0.488098     0.297331   
global        0.346131  0.402811   0.38517  0.292896   0.47507     0.324151   
sustain       0.351068  0.384581  0.385959  0.271927  0.485932     0.292679   
model         0.694206  0.761075  0.617515  0.686945  0.771095     0.606783   
imag          0.481729  0.505198  0.663423  0.527931  0.736457     0.493486   
use        5.26836e-09  0.452952  0.561825  0.402095  0.507767     0.466906   
class         0.452952         0  0.518334  0.210767  0.609299     0.469057   
data          0.561825  0.518334         0  0.408061  0.605955     0.378607   
dataset       0.402095  0.210767  0.408061         0  0.582237     0.394169   
learn         0.507767  0.609299  0.605955  0.582237         0     0.578129   
detect        0.466906  0.469057  0.378607  0.394169  0.578129  5.26836e-09   
perform       0.392909  0.507102  0.519989  0.405435  0.536292     0.376051   

               perform  
2020          0.361485  
covid         0.540009  
psycholog      0.31652  
pandem        0.358585  
impact        0.334573  
health        0.311642  
market         0.31498  
econom        0.319565  
global        0.306806  
sustain       0.316264  
model         0.466261  
imag          0.628683  
use           0.392909  
class         0.507102  
data          0.519989  
dataset       0.405435  
learn         0.536292  
detect        0.376051  
perform    5.26836e-09  
Pairwise-distance
               2020     covid psycholog    pandem    impact    health  \
2020              0  0.505063  0.672686  0.432156  0.512852  0.434606   
covid      0.505063         0  0.407881  0.401712  0.361303  0.328222   
psycholog  0.672686  0.407881         0  0.400096   0.42986  0.472691   
pandem     0.432156  0.401712  0.400096         0   0.24077  0.290657   
impact     0.512852  0.361303   0.42986   0.24077         0  0.314501   
health     0.434606  0.328222  0.472691  0.290657  0.314501         0   
market     0.781733  0.645259  0.550981   0.56435  0.596905   0.61346   
econom      0.56354  0.392692  0.486385  0.305459  0.107627  0.323205   
global      0.56389  0.422285  0.419721  0.208335  0.131422  0.305391   
sustain    0.586687  0.464904  0.462563  0.260812  0.122849  0.371079   
model       0.57884  0.400188  0.433394  0.304339  0.260406  0.266205   
imag       0.595126  0.425849  0.426358  0.291066  0.283301  0.263709   
use        0.633227  0.417661  0.295343  0.309249  0.316602  0.333201   
class      0.598411  0.425174  0.424199   0.29679  0.281428  0.285821   
data       0.604245  0.408811  0.356739  0.288379  0.297193  0.268307   
dataset    0.593534  0.425924  0.422955  0.295465  0.277013   0.27701   
learn      0.539011  0.420198  0.429224  0.262862   0.28251  0.262265   
detect     0.589569  0.425068  0.421899  0.291774  0.279492  0.276831   
perform    0.634514  0.486374  0.448863    0.3418  0.347315  0.344419   

                market    econom    global      sustain      model       imag  \
2020          0.781733   0.56354   0.56389     0.586687    0.57884   0.595126   
covid         0.645259  0.392692  0.422285     0.464904   0.400188   0.425849   
psycholog     0.550981  0.486385  0.419721     0.462563   0.433394   0.426358   
pandem         0.56435  0.305459  0.208335     0.260812   0.304339   0.291066   
impact        0.596905  0.107627  0.131422     0.122849   0.260406   0.283301   
health         0.61346  0.323205  0.305391     0.371079   0.266205   0.263709   
market     1.49012e-08  0.574963  0.556762     0.616914   0.488692   0.524363   
econom        0.574963         0  0.150939     0.151382    0.22559   0.264474   
global        0.556762  0.150939         0     0.102909   0.210995   0.210523   
sustain       0.616914  0.151382  0.102909  5.26836e-09   0.272011   0.275262   
model         0.488692   0.22559  0.210995     0.272011          0  0.0781712   
imag          0.524363  0.264474  0.210523     0.275262  0.0781712          0   
use           0.467781  0.327387  0.253201     0.317553   0.177739   0.158667   
class           0.5229  0.263481  0.214048     0.272265  0.0757704  0.0402415   
data          0.484607  0.293467  0.223786     0.298096   0.120077  0.0864051   
dataset       0.519603  0.257734  0.209978      0.26935  0.0661687  0.0296333   
learn         0.538972  0.280966  0.224054     0.282687   0.111026  0.0850276   
detect        0.523307  0.263573  0.212188     0.272973  0.0733222  0.0331416   
perform       0.561953  0.327319  0.270815     0.325933   0.211688   0.197754   

                   use        class       data      dataset        learn  \
2020          0.633227     0.598411   0.604245     0.593534     0.539011   
covid         0.417661     0.425174   0.408811     0.425924     0.420198   
psycholog     0.295343     0.424199   0.356739     0.422955     0.429224   
pandem        0.309249      0.29679   0.288379     0.295465     0.262862   
impact        0.316602     0.281428   0.297193     0.277013      0.28251   
health        0.333201     0.285821   0.268307      0.27701     0.262265   
market        0.467781       0.5229   0.484607     0.519603     0.538972   
econom        0.327387     0.263481   0.293467     0.257734     0.280966   
global        0.253201     0.214048   0.223786     0.209978     0.224054   
sustain       0.317553     0.272265   0.298096      0.26935     0.282687   
model         0.177739    0.0757704   0.120077    0.0661687     0.111026   
imag          0.158667    0.0402415  0.0864051    0.0296333    0.0850276   
use        2.63418e-09     0.154272  0.0884848     0.153277     0.179872   
class         0.154272  3.29272e-10  0.0988606    0.0226266    0.0745522   
data         0.0884848    0.0988606          0    0.0926619      0.12965   
dataset       0.153277    0.0226266  0.0926619  1.16415e-10    0.0757401   
learn         0.179872    0.0745522    0.12965    0.0757401  1.31709e-09   
detect        0.152052     0.022666  0.0934608    0.0147952    0.0659336   
perform       0.256703      0.19733   0.217991     0.194789     0.205128   

              detect      perform  
2020        0.589569     0.634514  
covid       0.425068     0.486374  
psycholog   0.421899     0.448863  
pandem      0.291774       0.3418  
impact      0.279492     0.347315  
health      0.276831     0.344419  
market      0.523307     0.561953  
econom      0.263573     0.327319  
global      0.212188     0.270815  
sustain     0.272973     0.325933  
model      0.0733222     0.211688  
imag       0.0331416     0.197754  
use         0.152052     0.256703  
class       0.022666      0.19733  
data       0.0934608     0.217991  
dataset    0.0147952     0.194789  
learn      0.0659336     0.205128  
detect             0     0.196208  
perform     0.196208  3.72529e-09

(-0.26948036532583, 0.92241, -1.3543149889229291)


# mantel test for covid science and non science dataset(Not necessary for experimental results of project)
def mantel_test_n(n):
    words_science = dict(model_science.show_topic(0, topn=word_count_science))
    words_nscience = dict(model_nscience.show_topic(0, topn=word_count_nscience))

    top_words_science = dict(model_science.show_topic(0, topn=n)) 
    top_words_nscience = dict(model_nscience.show_topic(0,topn=n)) 


    c={} 
    for key in top_words_science:
        try:
            c[key] = words_nscience[key]
        except KeyError:
            c[key] = 0

    top_words_nscience.update(c)

    b={} 
    for key in top_words_nscience:
        try:
            b[key] = words_science[key]
        except KeyError:
            b[key] = 0

    b.update(top_words_science)
    top_words_science=b

    #pairwise distance 
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_science =dict(model_science.show_topic(i, topn=word_count_science))
        b=pd.DataFrame(words_from_science,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_science.keys()), 
                      index = list(top_words_science.keys()))

    l = list(top_words_science.keys())

    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df1 = df.to_numpy()
    #pairwise distace 
    a=pd.DataFrame()

    for i in range(number_of_topics):
        words_from_nscience =dict(model_nscience.show_topic(i, topn=word_count_nscience))
        b=pd.DataFrame(words_from_nscience,index=[i])
        a=a.append(b)

    a=a.transpose()

    df = pd.DataFrame(columns=list(top_words_nscience.keys()), 
                      index = list(top_words_nscience.keys()))

    l = list(top_words_nscience.keys())
    print('Pairwise-distance')
    for i in l:
        for j in l:
            try:
                matrix=np.array([a.loc[i],a.loc[j]])
            except KeyError:
                matrix=np.zeros((2,3))
            n = s.metrics.pairwise.nan_euclidean_distances(matrix, matrix)
            df.loc[[i],[j]] = n[0,1]
    print(df)
    df2 = df.to_numpy()
    

    dist1 = list(df1[np.triu_indices(len(top_words_science), k = 1)])

    

    dist2 = list(df2[np.triu_indices(len(top_words_nscience), k = 1)])

    return mantel.test(dist1, dist2, perms=100000, method='pearson', tail='upper')


mantel_test_n(5)

Pairwise-distance
             test        peopl     covid   symptom      case     model  \
test            0     0.347187  0.598824  0.490315  0.708188  0.817206   
peopl    0.347187  2.63418e-09  0.495797  0.170185  0.478369  0.674606   
covid    0.598824     0.495797         0   0.48347  0.647243  0.703598   
symptom  0.490315     0.170185   0.48347         0  0.462958  0.642135   
case     0.708188     0.478369  0.647243  0.462958         0  0.703596   
model    0.817206     0.674606  0.703598  0.642135  0.703596         0   
imag     0.738193     0.617784  0.487238  0.542373   0.76704  0.936326   
use      0.599348      0.38579  0.464427  0.341378  0.423972  0.694206   
class    0.644169     0.434128  0.333658  0.379857  0.591853  0.761075   

             imag          use     class  
test     0.738193     0.599348  0.644169  
peopl    0.617784      0.38579  0.434128  
covid    0.487238     0.464427  0.333658  
symptom  0.542373     0.341378  0.379857  
case      0.76704     0.423972  0.591853  
model    0.936326     0.694206  0.761075  
imag            0     0.481729  0.505198  
use      0.481729  5.26836e-09  0.452952  
class    0.505198     0.452952         0  
Pairwise-distance
             test     peopl        covid   symptom      case     model imag  \
test            0  0.420708     0.690423  0.438307  0.623112  0.655078    0   
peopl    0.420708         0     0.517606  0.137618  0.441811  0.318337    0   
covid    0.690423  0.517606  1.05367e-08  0.531865  0.487958  0.474083    0   
symptom  0.438307  0.137618     0.531865         0  0.383702  0.249292    0   
case     0.623112  0.441811     0.487958  0.383702         0  0.377843    0   
model    0.655078  0.318337     0.474083  0.249292  0.377843         0    0   
imag            0         0            0         0         0         0    0   
use      0.681963  0.336553     0.459937  0.281959  0.366467  0.119021    0   
class           0         0            0         0         0         0    0   

              use class  
test     0.681963     0  
peopl    0.336553     0  
covid    0.459937     0  
symptom  0.281959     0  
case     0.366467     0  
model    0.119021     0  
imag            0     0  
use             0     0  
class           0     0

(0.04021584263963283, 0.45563, 0.14217850358220085)


mantel_test_n(10)

Pairwise-distance
                 test        peopl     covid    symptom      case      fever  \
test                0     0.347187  0.598824   0.490315  0.708188   0.489603   
peopl        0.347187  2.63418e-09  0.495797   0.170185  0.478369   0.176437   
covid        0.598824     0.495797         0    0.48347  0.647243   0.483824   
symptom      0.490315     0.170185   0.48347          0  0.462958  0.0202001   
case         0.708188     0.478369  0.647243   0.462958         0   0.476708   
fever        0.489603     0.176437  0.483824  0.0202001  0.476708          0   
travel       0.491367     0.176591  0.480921  0.0255815  0.478376  0.0191061   
respiratori  0.490401     0.179602   0.48307  0.0320539  0.469788  0.0291233   
contact      0.433678     0.117499  0.482128  0.0598675  0.474281  0.0613826   
worker       0.460651     0.147935  0.481982  0.0357449  0.481602  0.0324904   
model        0.817206     0.674606  0.703598   0.642135  0.703596   0.644656   
imag         0.738193     0.617784  0.487238   0.542373   0.76704   0.535371   
use          0.599348      0.38579  0.464427   0.341378  0.423972   0.344298   
class        0.644169     0.434128  0.333658   0.379857  0.591853   0.385153   
data         0.589202     0.407039  0.444024   0.390682    0.5335   0.393011   
dataset      0.474502     0.263405  0.323608   0.265979  0.519083   0.273091   
learn        0.733428     0.531913  0.585678   0.488368  0.690083   0.488483   
detect       0.551551     0.376064  0.482478   0.293118  0.555888   0.290483   
perform      0.584153     0.364401  0.540009   0.313099  0.569409   0.316373   

                  travel  respiratori    contact     worker     model  \
test            0.491367     0.490401   0.433678   0.460651  0.817206   
peopl           0.176591     0.179602   0.117499   0.147935  0.674606   
covid           0.480921      0.48307   0.482128   0.481982  0.703598   
symptom        0.0255815    0.0320539  0.0598675  0.0357449  0.642135   
case            0.478376     0.469788   0.474281   0.481602  0.703596   
fever          0.0191061    0.0291233  0.0613826  0.0324904  0.644656   
travel       2.32831e-10     0.040499   0.064308  0.0348743  0.641208   
respiratori     0.040499  6.58545e-10   0.068371  0.0489126  0.634773   
contact         0.064308     0.068371          0  0.0337389  0.649579   
worker         0.0348743    0.0489126  0.0337389          0  0.645047   
model           0.641208     0.634773   0.649579   0.645047         0   
imag            0.543357     0.527665   0.557435   0.548846  0.936326   
use              0.35092     0.325985    0.35306   0.355599  0.694206   
class            0.38565     0.390128   0.397687   0.388204  0.761075   
data            0.384323      0.40137   0.391187   0.389755  0.617515   
dataset         0.273946     0.276128   0.258653   0.263886  0.686945   
learn           0.474716     0.487677   0.500787   0.492966  0.771095   
detect          0.289378     0.286214   0.306398    0.29498  0.606783   
perform         0.317177     0.294292   0.323269   0.320012  0.466261   

                 imag          use     class      data   dataset     learn  \
test         0.738193     0.599348  0.644169  0.589202  0.474502  0.733428   
peopl        0.617784      0.38579  0.434128  0.407039  0.263405  0.531913   
covid        0.487238     0.464427  0.333658  0.444024  0.323608  0.585678   
symptom      0.542373     0.341378  0.379857  0.390682  0.265979  0.488368   
case          0.76704     0.423972  0.591853    0.5335  0.519083  0.690083   
fever        0.535371     0.344298  0.385153  0.393011  0.273091  0.488483   
travel       0.543357      0.35092   0.38565  0.384323  0.273946  0.474716   
respiratori  0.527665     0.325985  0.390128   0.40137  0.276128  0.487677   
contact      0.557435      0.35306  0.397687  0.391187  0.258653  0.500787   
worker       0.548846     0.355599  0.388204  0.389755  0.263886  0.492966   
model        0.936326     0.694206  0.761075  0.617515  0.686945  0.771095   
imag                0     0.481729  0.505198  0.663423  0.527931  0.736457   
use          0.481729  5.26836e-09  0.452952  0.561825  0.402095  0.507767   
class        0.505198     0.452952         0  0.518334  0.210767  0.609299   
data         0.663423     0.561825  0.518334         0  0.408061  0.605955   
dataset      0.527931     0.402095  0.210767  0.408061         0  0.582237   
learn        0.736457     0.507767  0.609299  0.605955  0.582237         0   
detect       0.493486     0.466906  0.469057  0.378607  0.394169  0.578129   
perform      0.628683     0.392909  0.507102  0.519989  0.405435  0.536292   

                  detect      perform  
test            0.551551     0.584153  
peopl           0.376064     0.364401  
covid           0.482478     0.540009  
symptom         0.293118     0.313099  
case            0.555888     0.569409  
fever           0.290483     0.316373  
travel          0.289378     0.317177  
respiratori     0.286214     0.294292  
contact         0.306398     0.323269  
worker           0.29498     0.320012  
model           0.606783     0.466261  
imag            0.493486     0.628683  
use             0.466906     0.392909  
class           0.469057     0.507102  
data            0.378607     0.519989  
dataset         0.394169     0.405435  
learn           0.578129     0.536292  
detect       5.26836e-09     0.376051  
perform         0.376051  5.26836e-09  
Pairwise-distance
                 test     peopl        covid    symptom      case      fever  \
test                0  0.420708     0.690423   0.438307  0.623112   0.482889   
peopl        0.420708         0     0.517606   0.137618  0.441811   0.161147   
covid        0.690423  0.517606  1.05367e-08   0.531865  0.487958   0.515485   
symptom      0.438307  0.137618     0.531865          0  0.383702  0.0571771   
case         0.623112  0.441811     0.487958   0.383702         0   0.385108   
fever        0.482889  0.161147     0.515485  0.0571771  0.385108          0   
travel       0.497285   0.17313     0.509594  0.0728513  0.379556  0.0177872   
respiratori  0.497285   0.17313     0.509594  0.0728513  0.379556  0.0177872   
contact      0.514832   0.27151     0.390354   0.215006  0.386427   0.183211   
worker       0.511895  0.186044     0.504262  0.0893207  0.374767  0.0355745   
model        0.655078  0.318337     0.474083   0.249292  0.377843   0.199416   
imag                0         0            0          0         0          0   
use          0.681963  0.336553     0.459937   0.281959  0.366467   0.235385   
class               0         0            0          0         0          0   
data         0.687995  0.366696      0.48532   0.305557   0.36574   0.262102   
dataset             0         0            0          0         0          0   
learn               0         0            0          0         0          0   
detect       0.650497  0.329389     0.481471   0.249021  0.348243   0.202921   
perform             0         0            0          0         0          0   

                  travel  respiratori   contact     worker      model imag  \
test            0.497285     0.497285  0.514832   0.511895   0.655078    0   
peopl            0.17313      0.17313   0.27151   0.186044   0.318337    0   
covid           0.509594     0.509594  0.390354   0.504262   0.474083    0   
symptom        0.0728513    0.0728513  0.215006  0.0893207   0.249292    0   
case            0.379556     0.379556  0.386427   0.374767   0.377843    0   
fever          0.0177872    0.0177872  0.183211  0.0355745   0.199416    0   
travel       3.72529e-09  3.72529e-09  0.176522  0.0177872   0.181951    0   
respiratori  3.72529e-09  3.72529e-09  0.176522  0.0177872   0.181951    0   
contact         0.176522     0.176522         0   0.171425   0.206653    0   
worker         0.0177872    0.0177872  0.171425          0   0.164554    0   
model           0.181951     0.181951  0.206653   0.164554          0    0   
imag                   0            0         0          0          0    0   
use              0.22021      0.22021  0.216514   0.205455   0.119021    0   
class                  0            0         0          0          0    0   
data            0.248441     0.248441  0.262947   0.235331    0.17412    0   
dataset                0            0         0          0          0    0   
learn                  0            0         0          0          0    0   
detect          0.185354     0.185354  0.214518   0.167834  0.0487495    0   
perform                0            0         0          0          0    0   

                  use class         data dataset learn     detect perform  
test         0.681963     0     0.687995       0     0   0.650497       0  
peopl        0.336553     0     0.366696       0     0   0.329389       0  
covid        0.459937     0      0.48532       0     0   0.481471       0  
symptom      0.281959     0     0.305557       0     0   0.249021       0  
case         0.366467     0      0.36574       0     0   0.348243       0  
fever        0.235385     0     0.262102       0     0   0.202921       0  
travel        0.22021     0     0.248441       0     0   0.185354       0  
respiratori   0.22021     0     0.248441       0     0   0.185354       0  
contact      0.216514     0     0.262947       0     0   0.214518       0  
worker       0.205455     0     0.235331       0     0   0.167834       0  
model        0.119021     0      0.17412       0     0  0.0487495       0  
imag                0     0            0       0     0          0       0  
use                 0     0     0.138263       0     0   0.127233       0  
class               0     0            0       0     0          0       0  
data         0.138263     0  2.63418e-09       0     0   0.170871       0  
dataset             0     0            0       0     0          0       0  
learn               0     0            0       0     0          0       0  
detect       0.127233     0     0.170871       0     0          0       0  
perform             0     0            0       0     0          0       0

(0.1288500457983727, 0.25871, 0.6828840309304662)


#loading dataset
def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    document = ''
    i=0
    with open( os.path.join(path, file_name) ,"r",encoding="utf8") as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for word in words:
                i=i+1
                document = document+word+' '
                if i == 200:
                    documents_list.append(document)
                    document,i = '',0
                    if len(documents_list) == 2:
                        break
    print("Total Number of Documents:",len(documents_list))
    #titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)


document_list,titles,word_count_science=load_data("","covid-science-small.txt")

Number of words in text file : 59691
Total Number of Documents: 2


#data preprocessing/cleaning
clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', 'tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte', 'guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'herrera', 'abstract', 'currently', 'coronavirus', 'disease', 'covid', 'most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans', 'chest', 'images', 'computed', 'tomography', 'scanners', 'testing', 'available', 'most', 'medical', 'centers', 'hence', 'many', 'cases', 'images', 'become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks', 'have', 'great', 'potential', 'building', 'covid', 'triage', 'systems', 'detecting', 'covid', 'patients', 'especially', 'patients', 'with', 'severity', 'unfortunately', 'current', 'databases', 'allow', 'building', 'such', 'systems', 'they', 'highly', 'heterogeneous', 'biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved', 'most', 'recent', 'covid', 'classification', 'models', 'under', 'close', 'collaboration', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levels', 'manuscript', 'received', 'september', '2020', 'revised', 'october', '2020', 'accepted', 'november', '2020', 'date']
['publication', 'november', '2020', 'date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported', 'project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'sars', 'covid', '2020', 'spanish', 'ministry', 'science', 'technology', 'under', 'project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal', 'programme', '2015', '18136', 'gómez', 'ríos', 'supported', 'programme', 'fpu16', '04765', 'charte', 'supported', 'programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research', 'council', 'grant', 'agreement', '647038', 'biodesert', 'this', 'project', 'approved', 'provincial', 'research', 'ethics', 'committee', 'granada', 'corresponding', 'author', 'siham', 'tabik', 'tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez', 'luengo', 'herrera', 'with', 'andalusian', 'research', 'institute', 'data', 'science', 'computational', 'intelligence', 'university', 'granada', '18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai', 'isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz', 'julianlm', 'decsai', 'herrera', 'decsai', 'martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova', 'olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínico', 'cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin', 'hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail', 'euolm', 'yahoo', 'area', 'with', 'atlanttic', 'research', 'center']


# LSA Model
number_of_topics=2
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_science =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values


#top words from the corpus
dict(model_science.show_topic(0, topn=5))

{'support': -0.3038609553429652,
 '2020': -0.23522191481735688,
 'granada': -0.2303377826106015,
 'covid': -0.21722636684891,
 'programm': -0.20257397022864343}


#wordcloud for top 5 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph8.png')


# Cosine similarity for top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled9.png')

cosine-similarity
           support      2020   granada     covid  programm
support          1  0.524928  0.955931 -0.115612         1
2020      0.524928         1  0.751685  0.784751  0.524928
granada   0.955931  0.751685         1  0.181107  0.955931
covid    -0.115612  0.784751  0.181107         1 -0.115612
programm         1  0.524928  0.955931 -0.115612         1


# networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig9.png')


#loading data
document_list,titles,word_count_nscience=load_data("","covid-non-science-small.txt")

Number of words in text file : 5231
Total Number of Documents: 2


# data preprocessing/clenaing
clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'reality', 'reuters', 'published', 'july', '2020', 'updated', 'july', '2020', 'marc', 'jones', 'london', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves', 'market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained', 'chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise', 'currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday', 'think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes', 'have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return']
['infection', 'rates', 'which', 'make', 'nervous', 'against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june', 'euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown', 'futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence', 'nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors', 'economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures', 'number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada', 'recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases']


# LSA Model
number_of_topics=2
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values


#dictionary of top 5 words
dict(model_nscience.show_topic(0, topn=5))

{'market': 0.2643530403063791,
 'rate': 0.1875484784084695,
 'juli': 0.1791266417580849,
 'covid': 0.1791266417580845,
 'june': 0.1704527970965883}


#wordcloud of top 5 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph9.png')


# Cosine similarity matrix of top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_covid_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_covid_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled10.png')

cosine-similarity
          market      rate      juli     covid      june
market         1  0.711061  0.771166  0.771166  0.459512
rate    0.711061         1   0.10071   0.10071  0.951241
juli    0.771166   0.10071         1         1  -0.21108
covid   0.771166   0.10071         1         1  -0.21108
june    0.459512  0.951241  -0.21108  -0.21108         1


#networkx graph of top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig10.png')


#loading data
document_list,titles,word_count_psy=load_data("","covid-psychology-small.txt")

Number of words in text file : 39735
Total Number of Documents: 2


#data preprocessing/cleaning
clean_text=preprocess_data(document_list)

['international', 'sociology', '2021', 'author', '2020', 'article', 'reuse', 'guidelines', 'sagepub', 'journals', 'permissions', '1177', '0268580920948807', 'journals', 'sagepub', 'home', 'psychology', 'politics', 'covid', 'misinfodemics', 'people', 'believe', 'misinfodemics', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'lahore', 'pakistan', 'abstract', 'misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse', 'health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate', 'evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects', 'misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier', 'about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this', 'review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects', 'responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article', 'shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product', 'psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive', 'crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations', 'psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications', 'development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health', 'keywords', 'conspiracy', 'theories', 'covid']
['health', 'communication', 'misinformation', 'mistrust', 'science', 'mechanisms', 'misinfodemics', 'this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding', 'corresponding', 'author', 'sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore', 'punjab', '54770', 'pakistan', 'email', 'sonia', 'mukhtar12', 'gmail', '948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar', 'research', 'article2020', 'article', 'international', 'sociology', 'covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus', 'covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern', 'globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin', 'unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during', 'lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication', 'become', 'major', 'source', 'interaction', 'holman', '2020', 'covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges', 'healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable', 'misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories', 'claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science', 'times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false', 'context', 'manipulated', 'data']


# LSA Model
number_of_topics=2
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values


#top 5 words from documents
dict(model_psy.show_topic(0, topn=5))

{'misinfodem': 0.4936266655941649,
 'covid': 0.3571916107745936,
 'articl': 0.21002057347124484,
 'commun': 0.14717103730334816,
 'health': 0.1471710373033479}


#wordcloud of top 5 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph28.png')


# Cosine similarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled11.png')

cosine-similarity
           misinfodem     covid    articl    commun    health
misinfodem          1  0.977431  0.994116  0.761706  0.761706
covid        0.977431         1  0.948797  0.881393  0.881393
articl       0.994116  0.948797         1  0.687043  0.687043
commun       0.761706  0.881393  0.687043         1         1
health       0.761706  0.881393  0.687043         1         1


#NetworkX graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig11.png')


#mantel test for 10 word network
mantel_test(5)

Pairwise-distance
           misinfodem     covid    articl commun health   support      2020  \
misinfodem          0         0         0      0      0         0         0   
covid               0         0  0.360337      0      0  0.608033  0.282616   
articl              0  0.360337         0      0      0  0.404283  0.208896   
commun              0         0         0      0      0         0         0   
health              0         0         0      0      0         0         0   
support             0  0.608033  0.404283      0      0         0  0.327071   
2020                0  0.282616  0.208896      0      0  0.327071         0   
granada             0  0.457677  0.252649      0      0  0.161988  0.175514   
programm            0  0.526543  0.280864      0      0  0.126325  0.246469   

              granada   programm  
misinfodem          0          0  
covid        0.457677   0.526543  
articl       0.252649   0.280864  
commun              0          0  
health              0          0  
support      0.161988   0.126325  
2020         0.175514   0.246469  
granada             0  0.0742376  
programm    0.0742376          0  
Pairwise-distance
           misinfodem     covid    articl     commun     health support  \
misinfodem          0    0.1894  0.293937   0.419015   0.419015       0   
covid          0.1894         0  0.156809   0.234496   0.234496       0   
articl       0.293937  0.156809         0   0.170443   0.170443       0   
commun       0.419015  0.234496  0.170443          0          0       0   
health       0.419015  0.234496  0.170443          0          0       0   
support             0         0         0          0          0       0   
2020         0.468991  0.293937    0.1894  0.0784044  0.0784044       0   
granada             0         0         0          0          0       0   
programm            0         0         0          0          0       0   

                 2020 granada programm  
misinfodem   0.468991       0        0  
covid        0.293937       0        0  
articl         0.1894       0        0  
commun      0.0784044       0        0  
health      0.0784044       0        0  
support             0       0        0  
2020                0       0        0  
granada             0       0        0  
programm            0       0        0

(-0.2789157231857564, 0.87744, -1.1428059907158423)


#mantel test for 20 word network
mantel_test(10)

Pairwise-distance
           misinfodem     covid    articl commun health mechan psycholog  \
misinfodem          0         0         0      0      0      0         0   
covid               0         0  0.360337      0      0      0         0   
articl              0  0.360337         0      0      0      0         0   
commun              0         0         0      0      0      0         0   
health              0         0         0      0      0      0         0   
mechan              0         0         0      0      0      0         0   
psycholog           0         0         0      0      0      0         0   
strategi            0         0         0      0      0      0         0   
misinform           0         0         0      0      0      0         0   
inform              0         0         0      0      0      0         0   
support             0  0.608033  0.404283      0      0      0         0   
2020                0  0.282616  0.208896      0      0      0         0   
granada             0  0.457677  0.252649      0      0      0         0   
research            0  0.526543  0.280864      0      0      0         0   
programm            0  0.526543  0.280864      0      0      0         0   
tabik               0  0.421418  0.189487      0      0      0         0   
garcía              0  0.318922  0.129222      0      0      0         0   
decsai              0  0.492938  0.220392      0      0      0         0   
project             0  0.492938  0.220392      0      0      0         0   

           strategi misinform inform   support       2020    granada  \
misinfodem        0         0      0         0          0          0   
covid             0         0      0  0.608033   0.282616   0.457677   
articl            0         0      0  0.404283   0.208896   0.252649   
commun            0         0      0         0          0          0   
health            0         0      0         0          0          0   
mechan            0         0      0         0          0          0   
psycholog         0         0      0         0          0          0   
strategi          0         0      0         0          0          0   
misinform         0         0      0         0          0          0   
inform            0         0      0         0          0          0   
support           0         0      0         0   0.327071   0.161988   
2020              0         0      0  0.327071          0   0.175514   
granada           0         0      0  0.161988   0.175514          0   
research          0         0      0  0.126325   0.246469  0.0742376   
programm          0         0      0  0.126325   0.246469  0.0742376   
tabik             0         0      0  0.220392   0.148475  0.0631623   
garcía            0         0      0  0.323975  0.0843519   0.161988   
decsai            0         0      0  0.189487   0.222713  0.0843519   
project           0         0      0  0.189487   0.222713  0.0843519   

             research   programm        tabik     garcía     decsai    project  
misinfodem          0          0            0          0          0          0  
covid        0.526543   0.526543     0.421418   0.318922   0.492938   0.492938  
articl       0.280864   0.280864     0.189487   0.129222   0.220392   0.220392  
commun              0          0            0          0          0          0  
health              0          0            0          0          0          0  
mechan              0          0            0          0          0          0  
psycholog           0          0            0          0          0          0  
strategi            0          0            0          0          0          0  
misinform           0          0            0          0          0          0  
inform              0          0            0          0          0          0  
support      0.126325   0.126325     0.220392   0.323975   0.189487   0.189487  
2020         0.246469   0.246469     0.148475  0.0843519   0.222713   0.222713  
granada     0.0742376  0.0742376    0.0631623   0.161988  0.0843519  0.0843519  
research            0          0     0.109024   0.218047  0.0631623  0.0631623  
programm            0          0     0.109024   0.218047  0.0631623  0.0631623  
tabik        0.109024   0.109024  3.72529e-09   0.109024  0.0742376  0.0742376  
garcía       0.218047   0.218047     0.109024          0   0.175514   0.175514  
decsai      0.0631623  0.0631623    0.0742376   0.175514          0          0  
project     0.0631623  0.0631623    0.0742376   0.175514          0          0  
Pairwise-distance
           misinfodem     covid     articl     commun     health     mechan  \
misinfodem          0    0.1894   0.293937   0.419015   0.419015   0.419015   
covid          0.1894         0   0.156809   0.234496   0.234496   0.234496   
articl       0.293937  0.156809          0   0.170443   0.170443   0.170443   
commun       0.419015  0.234496   0.170443          0          0          0   
health       0.419015  0.234496   0.170443          0          0          0   
mechan       0.419015  0.234496   0.170443          0          0          0   
psycholog    0.360699  0.235213  0.0784044   0.185806   0.185806   0.185806   
strategi     0.360699  0.235213  0.0784044   0.185806   0.185806   0.185806   
misinform    0.505066  0.315666   0.284614   0.115951   0.115951   0.115951   
inform       0.409223  0.240466   0.126266  0.0762243  0.0762243  0.0762243   
support             0         0          0          0          0          0   
2020         0.468991  0.293937     0.1894  0.0784044  0.0784044  0.0784044   
granada             0         0          0          0          0          0   
research     0.529541  0.350635   0.252533   0.120233   0.120233   0.120233   
programm            0         0          0          0          0          0   
tabik               0         0          0          0          0          0   
garcía              0         0          0          0          0          0   
decsai              0         0          0          0          0          0   
project             0         0          0          0          0          0   

            psycholog   strategi    misinform     inform support       2020  \
misinfodem   0.360699   0.360699     0.505066   0.409223       0   0.468991   
covid        0.235213   0.235213     0.315666   0.240466       0   0.293937   
articl      0.0784044  0.0784044     0.284614   0.126266       0     0.1894   
commun       0.185806   0.185806     0.115951  0.0762243       0  0.0784044   
health       0.185806   0.185806     0.115951  0.0762243       0  0.0784044   
mechan       0.185806   0.185806     0.115951  0.0762243       0  0.0784044   
psycholog           0          0     0.300211   0.115951       0   0.170443   
strategi            0          0     0.300211   0.115951       0   0.170443   
misinform    0.300211   0.300211  3.72529e-09   0.185806       0   0.152449   
inform       0.115951   0.115951     0.185806          0       0  0.0631332   
support             0          0            0          0       0          0   
2020         0.170443   0.170443     0.152449  0.0631332       0          0   
granada             0          0            0          0       0          0   
research     0.229409   0.229409      0.14117   0.126266       0  0.0631332   
programm            0          0            0          0       0          0   
tabik               0          0            0          0       0          0   
garcía              0          0            0          0       0          0   
decsai              0          0            0          0       0          0   
project             0          0            0          0       0          0   

           granada   research programm tabik garcía decsai project  
misinfodem       0   0.529541        0     0      0      0       0  
covid            0   0.350635        0     0      0      0       0  
articl           0   0.252533        0     0      0      0       0  
commun           0   0.120233        0     0      0      0       0  
health           0   0.120233        0     0      0      0       0  
mechan           0   0.120233        0     0      0      0       0  
psycholog        0   0.229409        0     0      0      0       0  
strategi         0   0.229409        0     0      0      0       0  
misinform        0    0.14117        0     0      0      0       0  
inform           0   0.126266        0     0      0      0       0  
support          0          0        0     0      0      0       0  
2020             0  0.0631332        0     0      0      0       0  
granada          0          0        0     0      0      0       0  
research         0          0        0     0      0      0       0  
programm         0          0        0     0      0      0       0  
tabik            0          0        0     0      0      0       0  
garcía           0          0        0     0      0      0       0  
decsai           0          0        0     0      0      0       0  
project          0          0        0     0      0      0       0

(-0.16544931052577935, 0.87239, -1.119178592676496)


mantel_test_n(5)

Pairwise-distance
         market rate juli     covid june   support      2020    granada  \
market        0    0    0         0    0         0         0          0   
rate          0    0    0         0    0         0         0          0   
juli          0    0    0         0    0         0         0          0   
covid         0    0    0         0    0  0.608033  0.282616   0.457677   
june          0    0    0         0    0         0         0          0   
support       0    0    0  0.608033    0         0  0.327071   0.161988   
2020          0    0    0  0.282616    0  0.327071         0   0.175514   
granada       0    0    0  0.457677    0  0.161988  0.175514          0   
programm      0    0    0  0.526543    0  0.126325  0.246469  0.0742376   

           programm  
market            0  
rate              0  
juli              0  
covid      0.526543  
june              0  
support    0.126325  
2020       0.246469  
granada   0.0742376  
programm          0  
Pairwise-distance
            market      rate       juli      covid      june support  \
market           0  0.197323   0.172313   0.172313  0.323988       0   
rate      0.197323         0   0.331629   0.331629  0.129455       0   
juli      0.172313  0.331629          0          0  0.459925       0   
covid     0.172313  0.331629          0          0  0.459925       0   
june      0.323988  0.129455   0.459925   0.459925         0       0   
support          0         0          0          0         0       0   
2020      0.174022  0.286306  0.0801315  0.0801315  0.409594       0   
granada          0         0          0          0         0       0   
programm         0         0          0          0         0       0   

               2020 granada programm  
market     0.174022       0        0  
rate       0.286306       0        0  
juli      0.0801315       0        0  
covid     0.0801315       0        0  
june       0.409594       0        0  
support           0       0        0  
2020              0       0        0  
granada           0       0        0  
programm          0       0        0

(-0.32116363224774647, 0.97195, -1.4419752809078545)


mantel_test_n(10)

Pairwise-distance
         market rate juli     covid june friday also infect job million  \
market        0    0    0         0    0      0    0      0   0       0   
rate          0    0    0         0    0      0    0      0   0       0   
juli          0    0    0         0    0      0    0      0   0       0   
covid         0    0    0         0    0      0    0      0   0       0   
june          0    0    0         0    0      0    0      0   0       0   
friday        0    0    0         0    0      0    0      0   0       0   
also          0    0    0         0    0      0    0      0   0       0   
infect        0    0    0         0    0      0    0      0   0       0   
job           0    0    0         0    0      0    0      0   0       0   
million       0    0    0         0    0      0    0      0   0       0   
support       0    0    0  0.608033    0      0    0      0   0       0   
2020          0    0    0  0.282616    0      0    0      0   0       0   
granada       0    0    0  0.457677    0      0    0      0   0       0   
research      0    0    0  0.526543    0      0    0      0   0       0   
programm      0    0    0  0.526543    0      0    0      0   0       0   
tabik         0    0    0  0.421418    0      0    0      0   0       0   
garcía        0    0    0  0.318922    0      0    0      0   0       0   
decsai        0    0    0  0.492938    0      0    0      0   0       0   
project       0    0    0  0.492938    0      0    0      0   0       0   

           support       2020    granada   research   programm        tabik  \
market           0          0          0          0          0            0   
rate             0          0          0          0          0            0   
juli             0          0          0          0          0            0   
covid     0.608033   0.282616   0.457677   0.526543   0.526543     0.421418   
june             0          0          0          0          0            0   
friday           0          0          0          0          0            0   
also             0          0          0          0          0            0   
infect           0          0          0          0          0            0   
job              0          0          0          0          0            0   
million          0          0          0          0          0            0   
support          0   0.327071   0.161988   0.126325   0.126325     0.220392   
2020      0.327071          0   0.175514   0.246469   0.246469     0.148475   
granada   0.161988   0.175514          0  0.0742376  0.0742376    0.0631623   
research  0.126325   0.246469  0.0742376          0          0     0.109024   
programm  0.126325   0.246469  0.0742376          0          0     0.109024   
tabik     0.220392   0.148475  0.0631623   0.109024   0.109024  3.72529e-09   
garcía    0.323975  0.0843519   0.161988   0.218047   0.218047     0.109024   
decsai    0.189487   0.222713  0.0843519  0.0631623  0.0631623    0.0742376   
project   0.189487   0.222713  0.0843519  0.0631623  0.0631623    0.0742376   

             garcía     decsai    project  
market            0          0          0  
rate              0          0          0  
juli              0          0          0  
covid      0.318922   0.492938   0.492938  
june              0          0          0  
friday            0          0          0  
also              0          0          0  
infect            0          0          0  
job               0          0          0  
million           0          0          0  
support    0.323975   0.189487   0.189487  
2020      0.0843519   0.222713   0.222713  
granada    0.161988  0.0843519  0.0843519  
research   0.218047  0.0631623  0.0631623  
programm   0.218047  0.0631623  0.0631623  
tabik      0.109024  0.0742376  0.0742376  
garcía            0   0.175514   0.175514  
decsai     0.175514          0          0  
project    0.175514          0          0  
Pairwise-distance
            market       rate       juli      covid       june     friday  \
market           0   0.197323   0.172313   0.172313   0.323988   0.104544   
rate      0.197323          0   0.331629   0.331629   0.129455   0.204797   
juli      0.172313   0.331629          0          0   0.459925   0.129455   
covid     0.172313   0.331629          0          0   0.459925   0.129455   
june      0.323988   0.129455   0.459925   0.459925          0   0.331629   
friday    0.104544   0.204797   0.129455   0.129455   0.331629          0   
also      0.104544   0.204797   0.129455   0.129455   0.331629          0   
infect    0.160263  0.0861564   0.258909   0.258909   0.204797   0.129455   
job       0.160263  0.0861564   0.258909   0.258909   0.204797   0.129455   
million   0.271949  0.0801315   0.388364   0.388364  0.0861564   0.258909   
support          0          0          0          0          0          0   
2020      0.174022   0.286306  0.0801315  0.0801315   0.409594  0.0861564   
granada          0          0          0          0          0          0   
research         0          0          0          0          0          0   
programm         0          0          0          0          0          0   
tabik            0          0          0          0          0          0   
garcía           0          0          0          0          0          0   
decsai           0          0          0          0          0          0   
project          0          0          0          0          0          0   

               also     infect        job    million support       2020  \
market     0.104544   0.160263   0.160263   0.271949       0   0.174022   
rate       0.204797  0.0861564  0.0861564  0.0801315       0   0.286306   
juli       0.129455   0.258909   0.258909   0.388364       0  0.0801315   
covid      0.129455   0.258909   0.258909   0.388364       0  0.0801315   
june       0.331629   0.204797   0.204797  0.0861564       0   0.409594   
friday            0   0.129455   0.129455   0.258909       0  0.0861564   
also              0   0.129455   0.129455   0.258909       0  0.0861564   
infect     0.129455          0          0   0.129455       0   0.204797   
job        0.129455          0          0   0.129455       0   0.204797   
million    0.258909   0.129455   0.129455          0       0   0.331629   
support           0          0          0          0       0          0   
2020      0.0861564   0.204797   0.204797   0.331629       0          0   
granada           0          0          0          0       0          0   
research          0          0          0          0       0          0   
programm          0          0          0          0       0          0   
tabik             0          0          0          0       0          0   
garcía            0          0          0          0       0          0   
decsai            0          0          0          0       0          0   
project           0          0          0          0       0          0   

         granada research programm tabik garcía decsai project  
market         0        0        0     0      0      0       0  
rate           0        0        0     0      0      0       0  
juli           0        0        0     0      0      0       0  
covid          0        0        0     0      0      0       0  
june           0        0        0     0      0      0       0  
friday         0        0        0     0      0      0       0  
also           0        0        0     0      0      0       0  
infect         0        0        0     0      0      0       0  
job            0        0        0     0      0      0       0  
million        0        0        0     0      0      0       0  
support        0        0        0     0      0      0       0  
2020           0        0        0     0      0      0       0  
granada        0        0        0     0      0      0       0  
research       0        0        0     0      0      0       0  
programm       0        0        0     0      0      0       0  
tabik          0        0        0     0      0      0       0  
garcía         0        0        0     0      0      0       0  
decsai         0        0        0     0      0      0       0  
project        0        0        0     0      0      0       0

(-0.245133288012048, 0.9991, -1.8229540903621335)


#data loading
def load_data(path,file_name):
    """
    Input  : path and file_name
    Purpose: loading text file
    Output : list of paragraphs/documents and
             title(initial 100 words considred as title of document)
    """
    documents_list = []
    titles=[]
    with open( os.path.join(path, file_name) ,"r",encoding='UTF8') as fin:
        data = fin.read()
        words = data.split()
        fin.seek(0)
        print('Number of words in text file :', len(words))
        for line in fin.readlines():
            text = line.strip()
            if text !='':
                documents_list.append(text)
    print("Total Number of Documents:",len(documents_list))
    titles.append( text[0:min(len(text),100)] )
    return documents_list,titles,len(words)


document_list,titles,word_count_science=load_data("","covid-science-small.txt")

Number of words in text file : 59691
Total Number of Documents: 7401


#data preprocessing
clean_text=preprocess_data(document_list)

['covidgr', 'dataset', 'covid', 'sdnet']
['methodology', 'predicting', 'covid']
['based', 'chest', 'images']
['tabik', 'gómez', 'ríos', 'martín', 'rodríguez', 'sevillano', 'garcía', 'area', 'charte']
['guirado', 'suárez', 'luengo', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'herrera']
['abstract', 'currently', 'coronavirus', 'disease', 'covid']
['most', 'infectious', 'diseases', '21st', 'century', 'diagnosed', 'using', 'testing', 'scans']
['chest', 'images', 'computed', 'tomography']
['scanners', 'testing', 'available', 'most']
['medical', 'centers', 'hence', 'many', 'cases', 'images']
['become', 'most', 'time', 'cost', 'effective', 'tool', 'assisting', 'clinicians', 'making', 'decisions', 'deep', 'learning', 'neural', 'networks']
['have', 'great', 'potential', 'building', 'covid', 'triage', 'systems']
['detecting', 'covid', 'patients', 'especially', 'patients', 'with']
['severity', 'unfortunately', 'current', 'databases', 'allow']
['building', 'such', 'systems', 'they', 'highly', 'heterogeneous']
['biased', 'towards', 'severe', 'cases', 'this', 'article', 'threefold', 'demystify', 'high', 'sensitivities', 'achieved']
['most', 'recent', 'covid', 'classification', 'models', 'under']
['close', 'collaboration', 'with', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', 'spain', 'built', 'covidgr', 'homogeneous', 'balanced', 'database', 'that', 'includes', 'levels']
['manuscript', 'received', 'september', '2020', 'revised', 'october', '2020']
['accepted', 'november', '2020', 'date', 'publication', 'november', '2020']
['date', 'current', 'version', 'december', '2020', 'this', 'work', 'supported']
['project', 'deepscop', 'ayudas', 'fundación', 'bbva', 'equipos', 'investigación', 'científica', 'data', '2018', 'covid19_rx', 'ayudas', 'fundación']
['bbva', 'equipos', 'investigación', 'científica', 'sars', 'covid']
['2020', 'spanish', 'ministry', 'science', 'technology', 'under']
['project', 'tin2017', '89517', 'tabik', 'supported', 'ramon', 'cajal']
['programme', '2015', '18136', 'gómez', 'ríos', 'supported']
['programme', 'fpu16', '04765', 'charte', 'supported']
['programme', 'fpu17', '04069', 'suárez', 'supported', 'programme', 'fpu18', '05989', 'supported', 'european', 'research']
['council', 'grant', 'agreement', '647038', 'biodesert', 'this', 'project']
['approved', 'provincial', 'research', 'ethics', 'committee', 'granada']
['corresponding', 'author', 'siham', 'tabik']
['tabik', 'gómez', 'ríos', 'sevillano', 'garcía', 'charte', 'suárez']
['luengo', 'herrera', 'with', 'andalusian', 'research', 'institute']
['data', 'science', 'computational', 'intelligence', 'university', 'granada']
['18071', 'granada', 'spain', 'mail', 'siham', 'anabelgrios', 'decsai']
['isega24ivan', 'gmail', 'fdavidcl', 'jlsuarezdiaz']
['julianlm', 'decsai', 'herrera', 'decsai']
['martín', 'rodríguez', 'valero', 'gonzález', 'garcía', 'villanova']
['olmedo', 'sánchez', 'with', 'hospital', 'universitario', 'clínico']
['cecilio', 'granada', '36310', 'spain', 'mail', 'joseluismartin']
['hotmail', 'valerogonzalez', 'yahoo', 'pgvillanova', 'gmail']
['euolm', 'yahoo']
['area', 'with', 'atlanttic', 'research', 'center', 'telecommunication', 'technologies', 'university', 'vigo', 'galicia', 'spain', 'mail']
['mreyarea', 'gmail']
['guirado', 'with', 'multidisciplinary', 'institute', 'environment']
['studies', 'ramón', 'margalef', 'university', 'alicante', '03690', 'spain', 'mail']
['geesecillo', 'gmail']
['digital', 'object', 'identifier', '1109', 'jbhi', '2020', '3037127']
['severity', 'from', 'normal', 'with', 'positive', 'mild', 'moderate', 'severe', 'covidgr', 'contains', 'positive']
['negative', 'posteroanterior', 'views']
['propose', 'covid', 'smart', 'data', 'based', 'network', 'covid', 'sdnet']
['methodology', 'improving', 'generalization', 'capacity']
['covid', 'classification', 'models', 'approach', 'reaches', 'good']
['stable', 'results', 'with', 'accuracy']
['severe', 'moderate']
['mild', 'covid', 'severity', 'levels', 'approach', 'could', 'help']
['early', 'detection', 'covid', 'covidgr', 'along', 'with']
['severity', 'level', 'labels', 'available', 'scientific', 'community', 'through', 'this', 'link', 'https', 'dasci', 'transferencia']
['open', 'data', 'covidgr']
['index', 'terms', 'covid', 'convolutional', 'neural', 'networks']
['smart', 'data']
['introduction']
[]
['last', 'months', 'world', 'been', 'witnessing']
['covid', 'pandemic', 'increasingly', 'infecting', 'large', 'mass']
['people', 'very', 'fast', 'everywhere', 'world', 'trends']
['clear', 'some', 'research', 'confirm', 'that', 'this', 'problem']
['persist', 'until', '2024', 'besides', 'prevalence', 'studies', 'conducted']
['several', 'countries', 'reveal', 'that', 'tiny', 'proportion', 'population']
['have', 'developed', 'antibodies', 'after', 'exposure', 'virus']
['spain', 'this', 'means', 'that', 'frequently', 'large', 'number', 'patients']
['will', 'need', 'assessed', 'small', 'time', 'intervals', 'number']
['clinicians', 'with', 'very', 'resources']
['general', 'covid', 'diagnosis', 'carried', 'using', 'least']
['these', 'three', 'tests', 'computed', 'tomography', 'scans', 'based', 'assessment']
['consists', 'analyzing', 'radiographic', 'images', 'from', 'different', 'angles', 'needed', 'equipment', 'this', 'assessment']
['available', 'most', 'hospitals', 'takes', 'more', 'than']
['minutes', 'patient', 'addition', 'time', 'required']
['decontamination']
['reverse', 'transcription', 'polymerase', 'chain', 'reaction', 'rtpcr', 'test', 'detects', 'viral', 'from', 'sputum']
['online', 'available', 'https', 'english', 'elpais', 'society', '2020']
['antibody', 'study', 'shows', 'just', 'spaniards', 'have', 'contracted', 'coronavirus']
['html', 'online', 'available', 'advocacy', 'economics', 'acrposition', 'statements', 'recommendations', 'chest', 'radiography', 'forsuspected', 'covid19', 'infection']
['ieee', '2020', 'this', 'article', 'free', 'access', 'download', 'along', 'with', 'rights', 'full', 'text', 'data', 'mining', 'analysis']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'july', '2021', 'from', 'ieee', 'xplore', 'restrictions', 'apply']
['3596', 'ieee', 'journal', 'biomedical', 'health', 'informatics', 'december', '2020']
['stratification', 'radiological', 'severity', 'covid', 'examples', 'rale', 'index', 'calculated']
['nasopharyngeal', 'swab', 'requires', 'specific', 'material']
['equipment', 'which', 'easily', 'accessible', 'takes']
['least', 'hours', 'which', 'desirable', 'positive', 'covid19', 'patients', 'should', 'identified', 'tracked', 'soon', 'possible', 'some', 'studies', 'found', 'that', 'results', 'from', 'several']
['tests', 'different', 'points', 'from', 'same', 'patients', 'were', 'variable', 'during', 'course', 'illness', 'producing', 'high', 'falsenegative', 'rate', 'authors', 'suggested', 'that', 'test']
['should', 'combined', 'with', 'other', 'clinical', 'tests', 'such']
['chest', 'required', 'equipment', 'this']
['assessment', 'less', 'cumbersome', 'lightweight']
['transportable', 'general', 'this', 'type', 'resources', 'more']
['available', 'than', 'required', 'scan', 'tests']
['addition', 'test', 'takes', 'about', 'seconds', 'patient']
['which', 'makes', 'most', 'time', 'cost', 'effective']
['assessment', 'tools']
['recent', 'studies', 'provide', 'estimates', 'expert', 'radiologists']
['sensitivity', 'diagnosis', 'covid', 'based', 'scans']
['study', 'patients', 'with', 'chest']
['essay', 'performed', 'within', 'days', 'reported']
['sensitivity', 'compared', 'with', 'sensitivity']
['different', 'study', 'patients', 'mean']
['years', 'reported', 'sensitivity', 'compared']
['with', 'initial', 'according', 'analysis']
['ambulatory', 'patients', 'most', 'patients', 'presenting', 'urgent', 'care']
['centers', 'with', 'confirmed', 'coronavirus', 'disease', '2019', 'have', 'normal']
['mildly', 'abnormal', 'findings', 'only', 'these', 'patients']
['correctly', 'diagnosed', 'expert']
['recent', 'study', 'authors', 'proposed', 'simplifying', 'quantification', 'level', 'severity', 'adapting', 'previously', 'defined']
['radiographic', 'assessment', 'lung', 'edema', 'rale', 'score']
['covid', 'this', 'score', 'calculated', 'assigning', 'value']
['between', 'each', 'lung', 'depending', 'extent', 'visual']
['features', 'such', 'consolidation', 'ground', 'glass', 'opacities']
['four', 'parts', 'each', 'lung', 'depicted', 'based', 'this', 'score']
['experts', 'identify', 'level', 'severity', 'infection', 'among']
['four', 'severity', 'stages', 'normal', 'mild', 'moderate']
['severe', 'practice', 'patient', 'classified', 'expert', 'radiologist']
['authorized', 'licensed', 'limited', 'university', 'hertfordshire', 'downloaded', 'july', '2021', 'from', 'ieee', 'xplore', 'restrictions', 'apply']
['tabik', 'covidgr', 'dataset', 'covid', 'sdnet', 'methodology', 'predicting', 'covid', 'based', 'chest', 'images', '3597']
['normal', 'have', 'positive', 'refer', 'these', 'cases']
['normal', 'expert', 'annotation', 'adopted', 'this', 'work', 'based']
['this', 'score']
['automated', 'image', 'analysis', 'deep', 'learning', 'models']
['have', 'great', 'potential', 'optimize', 'role', 'images']
['fast', 'diagnosis', 'covid', 'robust', 'accurate']
['model', 'could', 'serve', 'triage', 'method', 'support']
['medical', 'decision', 'making', 'increasing', 'number', 'recent', 'works']
['claim', 'achieving', 'impressive', 'sensitivities', 'higher', 'than']
['expert', 'radiologists', 'these', 'high', 'sensitivities', 'bias']
['most', 'used', 'covid', 'dataset', 'covid', 'image', 'data']
['collection', 'this', 'dataset', 'includes', 'very', 'small', 'number']
['covid', 'positive', 'cases', 'coming', 'from', 'highly', 'heterogeneous']
['sources', 'least', 'countries', 'most', 'cases', 'severe', 'patients']
['issue', 'that', 'drastically', 'reduces', 'clinical', 'value', 'populate']
['covid', 'healthy', 'classes', 'researchers', 'using']
['images', 'from', 'diverse', 'pulmonary', 'disease', 'repositories']
['obtained', 'models', 'will', 'have', 'clinical', 'value', 'well', 'since', 'they']
['will', 'unable', 'detect', 'patients', 'with', 'moderate', 'severity']
['which', 'target', 'clinical', 'triage', 'system', 'view', 'this']
['situation', 'there', 'still', 'huge', 'need', 'higher', 'quality', 'datasets', 'built']
['under', 'same', 'clinical', 'protocol', 'under', 'close', 'collaboration']
['with', 'expert', 'radiologists']
['multiple', 'studies', 'have', 'proven', 'that', 'higher', 'quality', 'data', 'ensures']
['higher', 'quality', 'models', 'concept', 'smart', 'data', 'refers']
['process', 'converting', 'data', 'into', 'higher', 'quality', 'data']
['with', 'higher', 'concentration', 'useful', 'information', 'smart', 'data']
['includes', 'processing', 'methods', 'that', 'improve', 'value']
['veracity', 'data', 'examples', 'these', 'methods', 'include', 'noise']
['elimination', 'data', 'augmentation', 'data', 'transformation']
['among', 'other', 'techniques']
['this', 'work', 'designed', 'high', 'clinical', 'quality', 'dataset']
['named', 'covidgr', 'that', 'includes', 'four', 'levels', 'severity']
['normal', 'mild', 'moderate', 'severe', 'identified', 'these']
['four', 'severity', 'levels', 'from', 'recent', 'covid', 'radiological', 'study']
['also', 'propose', 'covid', 'smart', 'data', 'based', 'network']
['covid', 'sdnet', 'methodology', 'combines', 'segmentation', 'dataaugmentation', 'data', 'transformations', 'together', 'with', 'appropriate', 'convolutional', 'neural', 'network', 'inference']
['contributions', 'this', 'paper', 'summarized', 'follows']
['analyze', 'reliability', 'potential', 'limitations', 'most']
['used', 'covid', 'datasets', 'models']
['from', 'data', 'perspective', 'provide', 'first', 'public', 'dataset']
['called', 'covidgr', 'that', 'quantifies', 'covid', 'terms']
['severity', 'levels', 'normal', 'mild', 'moderate', 'severe']
['with', 'building', 'triage', 'systems', 'with', 'high', 'clinical']
['value']
['from', 'processing', 'perspective', 'combined', 'several']
['methods', 'eliminate', 'irrelevant', 'information', 'from']
['input', 'images', 'used', 'processing', 'method']
['called', 'segmentation', 'based', 'cropping', 'increase', 'discrimination', 'capacity', 'classification', 'model', 'used']
['class', 'inherent', 'transformation', 'method', 'inspired', 'gans']
['from', 'post', 'processing', 'perspective', 'proposed']
['inference', 'process', 'that', 'fuses', 'predictions', 'four']
['transformed', 'classes', 'obtained', 'class', 'inherent', 'transformation', 'method', 'calculate', 'final', 'prediction']
['from', 'global', 'perspective', 'designed', 'novel', 'methodology', 'named', 'covid', 'sdnet', 'with', 'high', 'generalization', 'capacity', 'covid', 'classification', 'based']
['images', 'covid', 'sdnet', 'combines', 'segmentation', 'datatransformation', 'data', 'augmentation', 'suitable']
['model', 'together', 'with', 'inference', 'approach', 'final']
['prediction']
['experiments', 'demonstrate', 'that', 'approach', 'reaches', 'good']
['stable', 'results', 'especially', 'moderate', 'severe', 'levels', 'with']
['respectively', 'lower', 'accuracies', 'were', 'obtained', 'mild', 'normal', 'severity', 'levels']
['with', 'respectively']
['this', 'article', 'organized', 'follows', 'review', 'most', 'used']
['datasets', 'covid', 'classification', 'approaches', 'provided']
['section', 'section', 'describes', 'covidgr', 'built']
['organized', 'approach', 'presented', 'section', 'experiments', 'comparisons', 'results', 'provided', 'section']
['inspection', 'model', 'decision', 'using', 'heatmaps', 'provided']
['section', 'conclusions', 'pointed', 'section']
['related', 'works']
['last', 'months', 'have', 'known', 'increasing', 'number', 'works']
['exploring', 'potential', 'deep', 'learning', 'models', 'automating']
['covid', 'diagnosis', 'based', 'images', 'results']
['promising', 'still', 'much', 'work', 'needs', 'done', 'level']
['data', 'models', 'design', 'given', 'potential', 'bias', 'this', 'type']
['problems', 'several', 'studies', 'include', 'explication', 'methods', 'their']
['models', 'this', 'section', 'analyzes', 'advantages', 'limitations']
['current', 'datasets', 'models', 'building', 'automatic', 'covid']
['diagnosis', 'systems', 'with', 'without', 'decision', 'explication']
['datasets']
['there', 'does', 'exist', 'high', 'quality', 'collection']
['images', 'building', 'covid', 'diagnosis', 'systems', 'high', 'clinical', 'value', 'currently', 'main', 'source', 'covid', 'class']
['covid', 'image', 'data', 'collection', 'contains', 'positive']
['negative', 'views', 'these', 'images', 'were', 'obtained', 'from', 'highly']
['heterogeneous', 'equipment', 'from', 'around', 'world', 'another']
['example', 'covid', 'dataset', 'figure', 'covid', 'chest']
['dataset', 'initiative', 'build', 'covid', 'classes', 'most']
['studies', 'using', 'from', 'multiple', 'public', 'pulmonary']
['disease', 'data', 'sets', 'examples', 'these', 'repositories', 'rsna', 'pneumonia', 'challenge', 'dataset', 'kaggle']
['chestx', 'ray8', 'dataset']
['mimic', 'dataset']
['padchest', 'dataset']
['instance', 'covidx', 'built', 'combining', 'three']
['public', 'datasets', 'covid', 'image', 'data', 'collection']
['figure', 'covid', 'chest', 'dataset', 'initiative']
['rsna', 'pneumonia', 'detection', 'challenge', 'dataset', 'covidx']
['built', 'organizing', 'covidx', 'into', 'three', 'classes']
['normal', 'healthy', 'pneumonia', 'covid', 'using']
['images', 'covid', 'class', 'including', 'posteroanterior']
['anteroposterior', 'views', 'seetable', 'notice', 'that', 'correct']
['learning', 'front', 'view', 'back', 'view', 'cannot', 'mixed']
['same', 'class']
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[]
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[]
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[]
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[]
[]
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[]
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['center', 'cropped', 'images', 'experiment', 'right', 'segmented', 'images', 'experiment']
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['every', 'single', 'exploration', 'framed', 'differently', 'sometimes']
['even', 'taken', 'with', 'different', 'machines', 'projections']
['potential', 'bias', 'expected', 'minimized']
['concerning', 'type', 'projection', 'evaluate']
['effectiveness', 'system', 'been', 'studied', 'taking', 'into']
['account', 'this', 'potential', 'variability', 'factor', 'which', 'consid', 'ered', 'most', 'significant', 'particular']
['table', 'presents', 'outcomes', 'after', 'accounting']
['influence', 'projection', 'perfor', 'mance', 'system', 'general', 'terms', 'system', 'demon', 'strates', 'consistency', 'with', 'respect', 'projection', 'used']
['differences', 'mainly', 'attributable', 'smaller', 'train', 'testing', 'sets', 'however', 'significant', 'differences']
['shown', 'projection', 'class', 'covid', 'experiment']
['decreasing', 'reason']
['unexpected', 'drop', 'performance', 'unknown', 'likely']
['226822', 'volume', '2020j', 'arias', 'londoño', 'artificial', 'intelligence', 'applied', 'chest', 'images', 'automatic', 'detection', 'covid']
['figure', 'mapping', 'high', 'dimensional', 'data', 'layer', 'adjacent', 'output', 'into', 'dimensional', 'plot', 'output', 'network', 'embedding']
['using', 'training', 'data', 'bottom', 'output', 'network', 'embedding', 'using', 'testing', 'data', 'left', 'original', 'images', 'experiment']
['center', 'cropped', 'images', 'experiment', 'right', 'segmented', 'images', 'experiment', 'labels', 'correspond', 'data', 'sets', 'classes']
['attributable', 'underrepresented', 'class', 'corpus']
['table']
['besides', 'table', 'shows', 'three', 'experiments', 'under']
['evaluation', 'covid', 'class', 'error', 'distribu', 'tion', 'with', 'respect', 'patient', 'technology']
['detector', 'dataset', 'projection', 'four', 'variability']
['factors', 'enumerated', 'results', 'show', 'that', 'error', 'distribution']
['committed', 'system', 'follows', 'with', 'minor', 'deviations']
['existing', 'proportion', 'samples', 'corpus', 'these', 'results']
['suggest', 'that', 'there', 'clear', 'bias', 'with', 'respect', 'these', 'poten', 'tial', 'variability', 'factors', 'least', 'covid', 'class', 'which']
['considered', 'worst', 'case', 'underrepresentation']
['similar', 'results', 'would', 'expected', 'control', 'pneumonia']
['classes', 'these', 'results', 'provided', 'lack']
['certain', 'labels', 'some', 'datasets', 'used', 'table']
['concerning', 'datasets', 'used', 'reasonably', 'well']
['balanced', 'table', 'with', 'certain', 'bias', 'normal', 'class']
['covid', 'pneumonia', 'classes', 'have', 'very', 'similar', 'average']
['ages', 'controls', 'have', 'lower', 'mean', 'assumption']
['been', 'that', 'differences', 'significantly', 'affecting']
['results', 'mentioned', 'difference', 'might', 'explain']
['normal', 'cluster', 'less', 'spread', 'than', 'other']
['case', 'specific', 'biases', 'have', 'been', 'found']
['errors', 'committed', 'system']
['additional', 'study', 'also', 'carried', 'evaluate']
['influence', 'potential', 'specificities', 'different', 'datasets']
['used', 'compile', 'corpus', 'variability', 'results']
['with', 'respect', 'datasets', 'merged', 'build', 'corpus', 'this']
['variability', 'factor', 'evaluated', 'using', 'different']
['plots', 'each', 'experiment', 'similar', 'than']
['differentiating', 'corresponding', 'cluster', 'each', 'dataset']
['class']
['results', 'different', 'datasets', 'classes', 'clearly']
['merged', 'adjacent', 'same', 'cluster', 'however', 'eral', 'datasets', 'report', 'lower', 'variability', 'certain', 'classes']
['variability', 'terms', 'scattering', 'this', 'especially']
['clear', 'chexpert', 'pneumonia', 'sets', 'which', 'cessfully', 'merged', 'with', 'corresponding', 'class', 'appear']
['volume', '2020', '226823j', 'arias', 'londoño', 'artificial', 'intelligence', 'applied', 'chest', 'images', 'automatic', 'detection', 'covid']
['table', 'percentage', 'testing', 'samples', 'error', 'distribution', 'with']
['respect', 'several', 'potential', 'variability', 'factors', 'covid', 'class']
['hits', 'represents', 'percentage', 'samples', 'every', 'factor', 'under']
['analysis', 'correctly', 'predicted']
['clearly', 'clustered', 'suggesting', 'that', 'these', 'datasets', 'have', 'certain']
['unknown', 'specific', 'characteristics', 'different', 'those']
['complementary', 'datasets', 'model', 'been', 'able', 'manage']
['this', 'aspect', 'factor', 'analyzed', 'further', 'studies']
['discussion', 'conclusion']
['this', 'study', 'evaluates', 'deep', 'learning', 'model', 'detection']
['covid', 'from', 'images', 'paper', 'provides', 'addi', 'tional', 'evidence', 'state', 'supporting', 'poten', 'tial', 'deep', 'learning', 'techniques', 'accurately', 'categorize']
['images', 'corresponding', 'control', 'pneumonia', 'covid']
['patients', 'these', 'three', 'classes', 'were', 'chosen', 'under']
['assumption', 'that', 'they', 'support', 'clinicians', 'making', 'better']
['decisions', 'establishing', 'potential', 'differential', 'strategies', 'patients', 'depending', 'their', 'cause', 'infection', 'ever', 'main', 'goal', 'paper', 'demonstrate']
['suitability', 'deep', 'learning', 'categorizing', 'images']
['make', 'thoughtful', 'evaluation', 'results', 'different']
['preprocessing', 'approaches', 'searching', 'better', 'explainability']
['interpretability', 'results', 'while', 'providing', 'evidence']
['potential', 'effects', 'that', 'might', 'bias', 'results']
['model', 'relies', 'covid', 'network', 'which']
['served', 'basis', 'developing', 'more', 'refined', 'archi', 'tecture', 'this', 'network', 'been', 'chosen', 'tailored']
['characteristics', 'given', 'previous', 'good', 'results', 'reported']
['other', 'researchers', 'covid', 'trained', 'with']
['corpus', 'compiled', 'using', 'data', 'gathered', 'from', 'different', 'sources']
['control', 'pneumonia', 'classes', 'with']
['samples', 'respectively', 'were', 'collected', 'from', 'naset', 'montgomery', 'crx8', 'chexpert', 'mimic', 'datasets']
['covid', 'class', 'collected', 'from', 'information']
['available', 'bimcv', 'hospitales', 'datasets']
['although', 'covid', 'class', 'only', 'contains', 'chest']
['images', 'developers', 'data', 'sources', 'continu', 'ously', 'adding', 'cases', 'respective', 'repositories']
['number', 'samples', 'expected', 'grow', 'future', 'despite']
['unbalance', 'covid', 'class', 'date']
['authors', 'knowledge', 'this', 'most', 'extensive', 'compilation']
['covid', 'images', 'based', 'open', 'repositories', 'despite', 'that']
['number', 'covid', 'images', 'still', 'considered', 'small']
['compared', 'other', 'classes', 'therefore', 'necessary']
['compensate', 'class', 'imbalance', 'modifying']
['network', 'architecture', 'including', 'regularization', 'components']
['last', 'dense', 'layers', 'this', 'weighted', 'categorical']
['cross', 'entropy', 'loss', 'function', 'used', 'compensate', 'this']
['effect', 'likewise', 'data', 'augmentation', 'techniques', 'were', 'used']
['pneumonia', 'covid', 'classes', 'generate', 'more', 'samples']
['these', 'underrepresented', 'classes', 'automatically']
['stand', 'that', 'automatic', 'diagnosis', 'much', 'more', 'than']
['classification', 'exercise', 'meaning', 'that', 'many', 'factors', 'have']
['considered', 'bring', 'these', 'techniques', 'clinical', 'practice']
['this', 'respect', 'there', 'classic', 'assumption', 'literature']
['that', 'associated', 'heat', 'maps', 'calculated', 'with', 'grad']
['techniques', 'provide', 'clinical', 'interpretation', 'results']
['which', 'unclear', 'practice', 'light', 'results', 'shown']
['heat', 'maps', 'depicted', 'show', 'that', 'experiment']
['must', 'carefully', 'interpreted', 'despite', 'high', 'performance']
['metrics', 'obtained', 'experiment', 'significant', 'areas', 'identi', 'fied', 'network', 'pointing', 'towards', 'certain', 'areas', 'with']
['clear', 'interest', 'diagnosis', 'such', 'corners']
['images', 'sternum', 'clavicles', 'from', 'clinical', 'point']
['view', 'this', 'biasing', 'results', 'means', 'that', 'other', 'approaches']
['necessary', 'force', 'network', 'focus', 'lung']
['area', 'this', 'respect', 'have', 'developed', 'compared']
['results', 'with', 'preprocessing', 'approaches', 'based', 'cropping']
['images', 'segmenting', 'lung', 'area', 'experiment']
['experiment', 'again', 'given', 'heat', 'maps', 'corresponding']
['experiment', 'also', 'similar', 'explainability', 'prob', 'lems', 'those', 'enumerated', 'experiment', 'image', 'area']
['reduction', 'proposed', 'experiment', 'significantly', 'decreases']
['system', 'performance', 'removing', 'metadata', 'that']
['usually', 'appears', 'left', 'right', 'corner', 'this', 'technique']
['removes', 'areas', 'that', 'help', 'categorize', 'images', 'have']
['interest', 'from', 'diagnosis', 'point', 'view', 'however', 'while']
['comparing', 'experiments', 'performance', 'results', 'improve']
['third', 'approach', 'which', 'focuses', 'same', 'region']
['interest', 'with', 'mask', 'that', 'forces', 'network']
['only', 'lungs', 'thus', 'results', 'obtained', 'experiments']
['suggest', 'that', 'eliminating', 'needless', 'features', 'extracted']
['from', 'background', 'related', 'regions', 'improves']
['results', 'besides', 'third', 'approach', 'experiment', 'provides']
['more', 'explainable', 'interpretative', 'results', 'with', 'network']
['focusing', 'attention', 'only', 'area', 'interest']
['disease', 'gain', 'explainability', 'last', 'method', 'still']
['cost', 'lower', 'accuracy', 'with', 'respect', 'experiment']
['improvement', 'explainability', 'interpretability']
['considered', 'critical', 'translating', 'these', 'techniques']
['clinical', 'setting', 'despite', 'decrease', 'performance']
['proposed', 'method', 'experiment', 'provided', 'promising']
['results', 'with', 'bacc']
[]
['performance', 'results', 'obtained', 'line', 'with', 'those', 'sented', 'which', 'reports', 'sensitivities']
['control', 'pneumonia', 'covid', 'classes', 'respectively']
['also', 'modeling', 'with', 'covid', 'similar', 'conditions']
['experiment', 'training', 'with', 'much', 'smaller', 'corpus']
['images', 'from', 'covid', 'patients']
['226824', 'volume', '2020j', 'arias', 'londoño', 'artificial', 'intelligence', 'applied', 'chest', 'images', 'automatic', 'detection', 'covid']
['controls', 'images', 'belonging', 'patients', 'with']
['different', 'types', 'pneumonia']
['paper', 'also', 'critically', 'evaluates', 'effect', 'several']
['variability', 'factors', 'that', 'might', 'compromise', 'network', 'formance', 'instance', 'projection', 'effect']
['evaluated', 'retraining', 'network', 'checking', 'comes', 'this', 'effect', 'important', 'given', 'that', 'projections']
['often', 'practiced', 'erect', 'positions', 'observe', 'pulmonary', 'ways']
['better', 'expected', 'examined', 'healthy', 'slightly']
['affected', 'patients', 'contrast', 'projections', 'often', 'ferred', 'patients', 'confined', 'such', 'expected']
['practised', 'most', 'severe', 'cases', 'since', 'projections']
['common', 'covid', 'patients', 'these', 'cases', 'more']
['blood', 'will', 'flow', 'lungs', 'apices', 'than', 'when', 'standing']
['thus', 'considering', 'this', 'variability', 'factor', 'result']
['misdiagnosis', 'pulmonary', 'congestion', 'indeed']
['obtained', 'results', 'have', 'highlighted', 'importance', 'taking']
['into', 'account', 'this', 'factor', 'when', 'designing', 'training', 'corpus']
['decreases', 'projections', 'experiments', 'with']
['covid', 'images', 'this', 'issue', 'probably', 'under', 'representation', 'this', 'class', 'table', 'which', 'would', 'require']
['further', 'specific', 'analysis', 'when', 'designing', 'future', 'corpora']
['other', 'hand', 'results', 'have', 'shown', 'that', 'error', 'distri', 'bution', 'covid', 'class', 'follows', 'similar', 'proportion']
['percentage', 'images', 'available', 'corpus', 'while', 'cate', 'gorizing', 'gender', 'detector', 'technology', 'projection']
['dataset', 'these', 'results', 'suggest', 'significant', 'bias', 'with']
['respect', 'these', 'potential', 'variability', 'factors', 'least']
['covid', 'class', 'which', 'less', 'represented']
['analysis', 'clusters', 'classes', 'were', 'distributed']
['also', 'presented', 'demonstrating', 'each', 'class']
['differentiated', 'these', 'plots', 'help', 'identify', 'existing', 'overlap']
['among', 'classes', 'especially', 'that', 'present', 'between', 'pneumonia']
['covid', 'lesser', 'extent', 'between', 'controls']
['pneumonia', 'similarly', 'since', 'corpus', 'used', 'train']
['network', 'built', 'around', 'several', 'datasets']
['plots', 'produced', 'differentiating', 'according', 'each']
['subsets', 'used', 'training', 'this', 'test', 'served']
['evaluate', 'influence', 'each', 'dataset', 'potential', 'specific']
['characteristics', 'training', 'procedure', 'hence', 'possible']
['sources', 'confusion', 'that', 'arise', 'particularities']
['corpora', 'that', 'tested', 'plots', 'suggest', 'that', 'different']
['datasets', 'correctly', 'merged', 'general', 'terms', 'with', 'some']
['exceptions', 'these', 'exceptions', 'suggest', 'that', 'there', 'might']
['certain', 'unknown', 'characteristics', 'datasets', 'used', 'which']
['cluster', 'images', 'belonging', 'same', 'dataset', 'together']
['covid', 'also', 'demonstrated', 'being', 'good', 'start', 'point', 'characterization', 'disease', 'employing']
['images', 'indeed', 'paper', 'outcomes', 'suggest', 'possibility']
['automatically', 'identify', 'lung', 'lesions', 'associated', 'with']
['covid', 'infection', 'analyzing', 'grad', 'mappings', 'experiment', 'providing', 'explainable']
['justification', 'about', 'network', 'works', 'however']
['interpretation', 'heat', 'maps', 'obtained', 'control']
['class', 'must', 'carried', 'carefully', 'whereas', 'areas']
['significant', 'interest', 'pneumonia', 'covid', 'classes']
['supposed', 'point', 'potential', 'lesions', 'with', 'higher', 'density']
['with', 'different', 'textures', 'contrast', 'controls', 'areas']
['significant', 'interest', 'classification', 'control', 'group']
['supposed', 'correspond', 'something', 'complementary']
['potentially', 'highlighting', 'less', 'dense', 'areas', 'thus', 'control']
['class', 'these', 'areas', 'point', 'towards', 'kind', 'lesion']
['lungs']
['likewise', 'system', 'developed', 'experiment', 'attains']
['comparable', 'results', 'those', 'achieved', 'human', 'evaluator']
['differentiating', 'pneumonia', 'from', 'covid', 'this', 'respect']
['ability', 'seven', 'radiologists', 'correctly', 'differentiate']
['pneumonia', 'covid', 'from', 'images', 'tested']
['results', 'indicated', 'that', 'radiologists', 'achieved', 'sitivities', 'ranging', 'from', 'mean', 'speci', 'ficities', 'ranging', 'from', 'mean', 'these', 'results']
['suggest', 'that', 'systems', 'have', 'potential', 'supervised']
['clinical', 'environment']
['covid', 'still', 'disease', 'much', 'remains']
['studied', 'deep', 'learning', 'techniques']
['would', 'potentially', 'help', 'understand', 'mechanisms']
['sars', 'cov2', 'attacks', 'lungs', 'alveoli']
['evolves', 'during', 'different', 'stages', 'ease', 'despite', 'there', 'some', 'empirical', 'evidence']
['evolution', 'covid', 'based', 'observations', 'made']
['radiologists', 'employment', 'automatic', 'techniques']
['based', 'machine', 'learning', 'would', 'help', 'analyze', 'data', 'sively', 'guide', 'research', 'onto', 'certain', 'paths', 'extract', 'conclu', 'sions', 'faster', 'nevertheless', 'more', 'interpretable', 'explainable']
['methods', 'required', 'step', 'forward']
['inline', 'with', 'previous', 'comment', 'based', 'empir', 'ical', 'evidence', 'respecting', 'evolution', 'disease']
['been', 'stated', 'that', 'during', 'early', 'stages', 'disease', 'ground', 'glass', 'shadows', 'pulmonary', 'consolidation', 'nodules']
['local', 'consolidation', 'centre', 'with', 'peripheral', 'ground', 'glass', 'density', 'often', 'observed', 'however', 'once', 'disease']
['evolves', 'consolidations', 'reduce', 'their', 'density', 'resembling']
['ground', 'glass', 'opacity', 'that', 'derive', 'white', 'lung']
['disease', 'worsens', 'minimization', 'opacities']
['course', 'disease', 'improves', 'this', 'manner']
['these', 'characteristic', 'behaviours', 'automatically']
['identified', 'would', 'possible', 'stratify', 'disorder', 'stage']
['according', 'severity', 'computing', 'extent', 'ground', 'glass', 'opacities', 'densities', 'would', 'also', 'useful', 'assess']
['severity', 'infection', 'evaluate', 'evolution']
['disease', 'this', 'regard', 'infection', 'extent', 'assessment']
['been', 'previously', 'tested', 'other', 'studies', 'covid']
['using', 'manual', 'procedures', 'based', 'observation', 'images']
['solutions', 'like', 'discussed', 'this', 'paper', 'intended']
['support', 'much', 'faster', 'diagnosis', 'alleviate', 'radiolo', 'gists', 'specialists', 'workload', 'substitute', 'their']
['assessment', 'rigorous', 'validation', 'would', 'open', 'door']
['integrating', 'these', 'algorithms', 'desktop', 'applications', 'cloud']
['servers', 'clinic', 'environment', 'thus']
['maintenance', 'update', 'would', 'cost', 'effective', 'straight', 'forward', 'would', 'reduce', 'healthcare', 'costs', 'improve']
['diagnosis', 'response', 'time', 'accuracy', 'case']
['volume', '2020', '226825j', 'arias', 'londoño', 'artificial', 'intelligence', 'applied', 'chest', 'images', 'automatic', 'detection', 'covid']
['deployment', 'these', 'algorithms', 'exempt', 'from']
['controversies', 'hosting', 'models', 'cloud', 'service']
['would', 'entail', 'uploading', 'images', 'that', 'might', 'subject']
['national', 'international', 'regulations', 'constraints']
['ensure', 'privacy']


# LSA Model
number_of_topics=100
words=word_count_science
model_science=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_law =dict(model_science.show_topic(0, topn=words))##dict and encoding matrix values


#dictionary of top 5 words
dict(model_science.show_topic(0, topn=5))

{'covid': 0.73821652382471,
 'model': 0.31135069890134603,
 'imag': 0.27205525106833406,
 'use': 0.19292326348390718,
 'detect': 0.1541950342665124}


#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_science.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph10.png')


# cosine similarity matrix for top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_science =dict(model_science.show_topic(i, topn=words))
    b=pd.DataFrame(words_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_science.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled12.png')

cosine-similarity
              covid        model         imag          use      detect
covid             1  0.000452072      0.00159  0.000898663   0.0115803
model   0.000452072            1   0.00101501  8.90285e-05  0.00275819
imag        0.00159   0.00101501            1 -0.000360897 -0.00105903
use     0.000898663  8.90285e-05 -0.000360897            1  0.00238728
detect    0.0115803   0.00275819  -0.00105903   0.00238728           1


#Networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='g',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig12.png')


#loading dataset
document_list,titles,word_count_nscience=load_data("","covid-non-science-small.txt")

Number of words in text file : 5231
Total Number of Documents: 229


#cleaning documents
clean_text=preprocess_data(document_list)

['covid', 'recovery', 'covid', 'reality']
['reuters']
['published', 'july', '2020', 'updated', 'july', '2020']
['marc', 'jones']
['london', 'july', 'reuters', 'world', 'shares', 'inched', 'towards', 'four', 'month', 'high', 'friday', 'industrial', 'bellwether', 'metal', 'copper', 'longest', 'weekly', 'winning', 'streak', 'nearly', 'three', 'years', 'recovering', 'global', 'data', 'kept', 'nagging', 'coronavirus', 'nerves']
['market', 'rally', 'fuelled', 'record', 'jobs', 'numbers', 'largely', 'blown', 'itself', 'amid', 'spike', 'covid', 'cases', 'though', 'fastest', 'expansion', 'china', 'services', 'sector', 'over', 'decade', 'more', 'stimulus', 'ensured', 'optimism', 'remained']
['chinese', 'shares', 'charged', 'their', 'highest', 'level', 'five', 'years', 'helping', 'asian', 'indexes', 'month', 'peaks', 'sight', 'european', 'markets', 'stalling', 'early', 'took', 'some', 'traders', 'surprise']
['currency', 'commodity', 'markets', 'also', 'subdued', 'feel', 'after', 'otherwise', 'strong', 'week', 'confidence', 'sensitive', 'stalwarts', 'such', 'copper', 'sterling', 'australian', 'dollar', 'which', 'struggled', 'friday']
['think', 'infection', 'rates', 'fears', 'localised', 'lockdowns', 'have', 'doused', 'some', 'enthusiasm', 'said', 'societe', 'generale', 'strategist', 'jukes']
['have', 'three', 'elements', 'vaccine', 'hopes', 'decent', 'data', 'most', 'places', 'also', 'return', 'infection', 'rates', 'which', 'make', 'nervous']
['against', 'basket', 'currencies', 'dollar', 'rose', 'slightly', 'early', 'london', 'trading', 'less', 'than', 'still', 'firmly', 'track', 'biggest', 'weekly', 'fall', 'since', 'first', 'week', 'june']
['euro', 'down', '1226', 'though', 'gained', 'against', 'safe', 'swiss', 'franc', 'fell', 'versus', 'sometimes', 'commodity', 'driven', 'norwegian', 'crown']
['futures', 'were', 'down', 'volumes', 'were', 'lower', 'than', 'usual', 'markets', 'holiday', 'friday', 'independence']
['nonfarm', 'payrolls', 'surged', 'million', 'jobs', 'june', 'above', 'average', 'forecast', 'million', 'jobs', 'june', 'thanks', 'rises', 'hard', 'hospitality', 'sectors']
['economists', 'noted', 'there', 'were', 'caveats', 'upbeat', 'headline', 'figures']
['number', 'permanent', 'losers', 'continued', 'rise', 'increasing', 'million', 'june', 'while', 'unemployment', 'rate', 'remains', 'chunky', 'percentage', 'points', 'above', 'february', 'level', 'deutsche', 'bank', 'analysis', 'unemployment', 'rate', 'behind', 'developed', 'market', 'peers', 'barring', 'canada']
['recovery', 'also', 'faces', 'more', 'headwinds', 'surge', 'coronavirus', 'infections', 'prompts', 'states', 'delay', 'some', 'cases', 'reverse', 'plans', 'stores', 'reopen', 'activities', 'resume']
['more', 'than', 'three', 'dozen', 'states', 'increases', 'covid', 'cases', 'with', 'cases', 'florida', 'spiking', 'above']
['nevertheless', 'markets', 'largely', 'overlooking', 'spikes', 'taking', 'view', 'that', 'overall', 'situation', 'still', 'improving', 'overall']
['year', 'german', 'government', 'bond', 'yields', 'basis', 'points', 'this', 'week', 'their', 'biggest', 'weekly', 'rise', 'month', 'though', 'they', 'nudged', 'down', 'friday', 'riskier', 'italian', 'yields', 'fell', 'well', 'though', 'which', 'their', 'lowest', 'since', 'late', 'march']
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['three', 'month', 'copper', 'hovering', 'tonne', 'more', 'than', 'from', 'lows', 'ploughed', 'march']
['issue', 'that', 'hangs', 'over', 'markets', 'will', 'surge', 'secondary', 'infections', 'that', 'will', 'trigger', 'second', 'wave', 'national', 'rather', 'than', 'regional', 'shutdowns', 'malcolm', 'freeman', 'director', 'kingdom', 'futures', 'wrote', 'note']
['reporting', 'marc', 'jones']
['newspaper']
['covid', 'vaccine', 'frontrunners']
['reuters']
['published', 'october', '2020', 'updated', 'october', '2020']
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['newspaper']
['giants', 'another', 'covid', 'case']
['reuters']
['published', 'november', '2020', 'updated', 'november', '2020']
['york', 'giants', 'have', 'another', 'case', 'covid']
['monday', 'night', 'were', 'notified', 'that', 'player', 'tested', 'positive', 'covid', 'giants', 'announced', 'tuesday', 'morning', 'player', 'immediately', 'self', 'isolated', 'contact', 'tracing', 'process', 'initiated', 'close', 'contacts', 'both', 'staff', 'members', 'were', 'identified', 'were', 'informed', 'remain', 'home', 'today']
['giants', 'entering', 'their', 'week', 'play', 'week', 'this', 'second', 'time', 'team', 'player', 'test', 'positive', 'covid']
['prior', 'playing', 'tampa', 'buccaneers', 'week', 'offensive', 'lineman', 'will', 'hernandez', 'tested', 'positive']
['hernandez', 'spent', 'nearly', 'weeks', 'reserve', 'covid', 'list', 'returned', 'team', 'last', 'week', 'played', 'sunday', 'victory', 'over', 'philadelphia', 'eagles']
['field', 'level', 'media']
['newspaper']
['covid', 'antivirals', 'taskforce', 'launched']
['media']
['published', 'april', '2021', 'updated', 'april', '2021']
['home', 'covid', 'treatments', 'being', 'sought', 'team', 'government', 'experts', 'speed', 'recovery', 'time', 'people', 'become', 'infected', 'with', 'virus']
['government', 'taskforce', 'supercharge', 'search', 'antiviral', 'medications', 'covid']
['prime', 'minister', 'boris', 'johnson', 'said', 'group', 'will', 'seek', 'medicines', 'stop', 'covid', 'tracks']
['hoped', 'that', 'antivirals', 'could', 'help', 'reduce', 'infections', 'limit', 'impact', 'variants']
['they', 'also', 'help', 'protect', 'people', 'cannot', 'take', 'vaccines', 'those', 'fully', 'protected', 'after', 'having']
['officials', 'hope', 'find', 'bring', 'treatments', 'online', 'this', 'year']
['this', 'could', 'mean', 'that', 'people', 'with', 'covid', 'those', 'they', 'have', 'been', 'close', 'contact', 'with', 'could', 'offered', 'antiviral', 'medication', 'offered', 'tablet', 'form', 'stop', 'infection', 'spreading', 'speed', 'recovery', 'time']
['antivirals', 'taskforce', 'which', 'appoint', 'chair', 'will', 'search', 'most', 'promising', 'potential', 'antiviral', 'medicines', 'which', 'taken', 'home']
['taskforce', 'will', 'support', 'development', 'drugs', 'through', 'clinical', 'trials', 'also', 'look', 'manufacturing', 'opportunities']
['antiviral', 'drugs', 'type', 'medication', 'used', 'specifically', 'treating', 'viral', 'infections', 'killing', 'preventing', 'growth', 'viruses']
['success', 'vaccination', 'programme', 'demonstrated', 'what', 'achieve', 'when', 'bring', 'together', 'brightest', 'minds', 'said', 'johnson']
['antivirals', 'taskforce', 'will', 'seek', 'develop', 'innovative', 'treatments', 'take', 'home', 'stop', 'covid', 'tracks']
['these', 'could', 'provide', 'another', 'vital', 'defence', 'against', 'future', 'increase', 'infections', 'save', 'more', 'lives']
['health', 'secretary', 'matt', 'hancock', 'said', 'medicines', 'vital', 'weapon', 'protect', 'loved', 'ones', 'from', 'this', 'terrible', 'virus']
['modelled', 'success', 'vaccines', 'therapeutics', 'taskforces', 'which', 'have', 'played', 'crucial', 'part', 'response', 'pandemic', 'bringing', 'together', 'team', 'that', 'will', 'supercharge', 'search', 'antiviral', 'treatments', 'roll', 'them', 'soon', 'autumn']
['government', 'chief', 'scientific', 'adviser', 'patrick', 'vallance', 'said', 'speed', 'which', 'vaccines', 'therapeutics', 'such', 'dexamethasone', 'have', 'been', 'identified', 'deployed', 'against', 'covid', 'been', 'critical', 'pandemic', 'response']
['antivirals', 'tablet', 'form', 'another', 'tool', 'response']
['they', 'could', 'help', 'protect', 'those', 'protected', 'ineligible', 'vaccines']
['they', 'could', 'also', 'another', 'layer', 'defence', 'face', 'variants', 'concern']
['nikita', 'kanani', 'medical', 'director', 'primary', 'care', 'england', 'said', 'research', 'into', 'reality', 'record', 'speed', 'during', 'pandemic', 'this', 'taskforce', 'will', 'help', 'identify', 'roll', 'even', 'more', 'convenient', 'treatments', 'patients', 'with', 'covid']
['commenting', 'announcement', 'penny', 'ward', 'visiting', 'professor', 'pharmaceutical', 'medicine', 'kings', 'college', 'london', 'said', 'antiviral', 'treatment', 'influenza', 'been', 'shown', 'reduce', 'hospitalisation', 'prevent', 'death', 'epidemic', 'pandemic', 'waves', 'similar', 'preferably', 'simple', 'antiviral', 'suitable', 'community', 'range', 'interventions', 'critical', 'enable', 'remain', 'covid', 'pandemic', 'even', 'event', 'emergence', 'viral', 'variants', 'insensitive', 'vaccines']
['said', 'that', 'there', 'number', 'early', 'phase', 'trials', 'looking', 'oral', 'antiviral', 'medications']
['newspaper']
['african', 'covid', 'deaths']
[]
['published', 'february', '2021', 'updated', 'february', '2021']
['africa', 'thursday', 'recorded', 'more', 'than', 'deaths', 'from', 'covid', 'grim', 'milestone', 'likely', 'understate', 'real', 'toll', 'continent', 'billion', 'people', 'battles', 'second', 'wave', 'infections']
['countries', 'region', 'have', 'death', 'toll', 'from', 'reported', 'cases', 'according', 'tally']
['continent', 'relatively', 'spared', 'pandemic', 'last', 'except', 'oceania', 'reach', 'threshold', 'deaths', 'which', 'europe', 'crossed', 'april', '2020']
['south', 'africa', 'worst', 'african', 'country', 'rolled', 'mass', 'testing', 'campaign', 'start', 'pandemic']
['date', 'country', 'recorded', 'nearly', 'million', 'cases', 'more', 'than', 'deaths']
['those', 'figures', 'based', 'daily', 'reports', 'communicated', 'health', 'authorities', 'only', 'reflect', 'fraction', 'actual', 'case', 'load', 'health', 'specialists']
['cases', 'clearly', 'under', 'reported', 'because', 'poor', 'access', 'healthcare', 'facilities', 'under', 'reporting', 'milder', 'cases', 'south', 'african', 'virologist', 'barry', 'schoub', 'also', 'member', 'scientific', 'council', 'south', 'african', 'ministry', 'health', 'told']
['understaffed', 'health', 'facilities', 'lack', 'means', 'have', 'meant', 'many', 'african', 'countries', 'have', 'been', 'unable', 'mass', 'testing']
['many', 'countries', 'have', 'mainly', 'tests', 'capitals', 'further', 'moves', 'away', 'from', 'urban', 'centres', 'less', 'there', 'tests', 'explained', 'french', 'epidemiologist', 'emmanuel', 'baron', 'from', 'doctors', 'without', 'borders']
['disease', 'that', 'unnoticed', 'with', 'asymptomatic', 'patients', 'with', 'symptoms', 'that', 'confused', 'with', 'others', 'added']
['covid', 'found', 'pawpaw']
['zimbabwe', 'country', 'with', 'devastated', 'economy', 'mismanaged', 'health', 'system', 'hospitals', 'filled', 'with', 'covid', 'patients', 'exhausted', 'doctors', 'overwhelmed', 'nurses', 'official', 'number', 'cases', 'remains']
['tanzania', 'stopped', 'testing', '2020', 'after', 'claiming', 'found', 'positive', 'covid', 'case', 'pawpaw', 'quail', 'even', 'goat', 'tanzanian', 'government', 'last', 'released', 'official', 'figures', 'april']
['someone', 'told', 'year', 'that', 'continent', 'would', 'deaths', 'from', 'this', 'infection', 'probably', 'would', 'have', 'believed', 'john', 'nkengasong', 'africa', 'director', 'centers', 'disease', 'control', 'preventions', 'told', 'reporters', 'thursday']
['toll', 'africa', 'however', 'significantly', 'lower', 'than', 'europe', 'world', 'most', 'affected', 'region', 'with', 'deaths', 'recorded', 'other', 'regions', 'that', 'badly', 'latin', 'america', 'with', 'deaths', 'united', 'states', 'canada', 'deaths']
['after', 'sharp', 'increase', 'january', 'africa', 'figures', 'have', 'fallen', 'sharply', 'past', 'weeks', 'over', 'last', 'seven', 'days', 'continent', 'recorded', 'deaths', 'drop', 'percent', 'from', 'previous', 'week']
['height', 'pandemic', 'january', 'continent', 'deaths']
['disaster']
['while', 'coronavirus', 'figures', 'clearly', 'underestimated', 'have', 'seen', 'health', 'disaster', 'africa', 'date', 'said', 'baron']
['several', 'studies', 'antibodies', 'which', 'make', 'possible', 'detect', 'whether', 'person', 'recovered', 'previously', 'been', 'exposed', 'virus', 'underway', 'many', 'african', 'countries', 'should', 'provide', 'better', 'idea', 'impact', 'pandemic', 'region']
['south', 'africa', 'where', 'almost', 'latest', 'cases', 'attributed', 'variant', 'virus', 'known', 'more', 'contagious', 'which', 'spread', 'widely', 'represents', 'nearly', 'half', 'deaths', 'reported', 'cases', 'continent']
['other', 'african', 'countries', 'that', 'most', 'affected', 'egypt', 'deaths', 'from', 'cases', 'morocco', 'deaths', 'from', 'cases']
['south', 'africa', 'also', 'country', 'with', 'most', 'covid', 'fatalities', 'continent', 'counting', 'deaths', 'inhabitants', 'ahead', 'tunisia', 'deaths', 'eswatini']
['lagging', 'behind', 'vaccination', 'race', 'continent', 'leading', 'industrial', 'powerhouse', 'administered', 'first', 'vaccines', 'wednesday']
['globally', 'covid', 'caused', 'more', 'than', 'million', 'infections', 'over', 'million', 'deaths', 'since', 'start', 'epidemic', 'wuhan', 'china', 'december', '2019']
[]
['newspaper']
['gyms', 'aren', 'covid', 'hotspots', 'industry']
['australian', 'associated', 'press']
['published', 'august', '2020', 'updated', 'august', '2020']
['fitness', 'industry', 'back', 'perception', 'that', 'gyms', 'covid', 'hotspots', 'saying', 'there', 'been', 'transmission', 'virus', 'linked', 'gyms']
['fitness', 'australia', 'chief', 'executive', 'barrie', 'elvish', 'said', 'fitness', 'australia', 'gathered', 'data', 'from', 'random', 'sample', 'operators', 'that', 'proves', 'gyms', 'safe']
['since', 'gyms', 'reopened', 'june', 'there', 'been', 'more', 'than', 'million', 'visits', 'gyms', 'with', 'recorded', 'community', 'transmission', 'said']
['same', 'cannot', 'said', 'restaurants', 'bars', 'where', 'cluster', 'cases', 'have', 'started', 'spread', 'however', 'gyms', 'still', 'being', 'categorised', 'with', 'these', 'other', 'entertainment', 'venues', 'cast', 'negative', 'light', 'said', 'statement', 'thursday']
['however', 'misinformation', 'negative', 'commentary', 'portrayed', 'health', 'fitness', 'facilities', 'hotspots', 'covid', 'resulting', 'some', 'people', 'being', 'scared', 'exercise', 'their', 'said', 'despite', 'additional', 'precautions', 'stringent', 'hygiene', 'practices']
['fitness', 'australia', 'data', 'gathered', 'from', 'electronic', 'swipes', 'used', 'members', 'access', 'which', 'also', 'could', 'used', 'sophisticated', 'contact', 'tracing', 'should', 'required']
['elvish', 'said', 'there', 'been', 'reported', 'cases', 'where', 'member', 'visited', 'while', 'infected', 'with', 'covid', 'health', 'authorities', 'identified', 'cases', 'transmission']
['part', 'problem', 'actually', 'part', 'solution', 'getting', 'more', 'australians', 'more', 'active', 'more', 'often', 'help', 'prevent', 'long', 'term', 'lifestyle', 'related', 'disease', 'elvish', 'said']
['urging', 'government', 'work', 'with', 'fitness', 'australia', 'industry', 'ensure', 'health', 'safety', 'community']
['newspaper']
['covid', 'victims', 'maintain', 'immunity']
['australian', 'associated', 'press']
['published', 'november', '2020', 'updated', 'november', '2020']
['australian', 'researchers', 'have', 'discovered', 'that', 'patients', 'been', 'infected', 'with', 'covid', 'retain', 'immunity', 'against', 'virus', 'disease', 'least', 'eight', 'months']
['research', 'strongest', 'evidence', 'that', 'vaccines', 'against', 'virus', 'will', 'work', 'long', 'periods']
['previous', 'studies', 'found', 'first', 'wave', 'antibodies', 'produced', 'human', 'body', 'after', 'infection', 'waned', 'after', 'first', 'months', 'raising', 'concerns', 'that', 'people', 'could', 'quickly', 'lose', 'immunity']
['research', 'allays', 'those', 'concerns']
['study', 'result', 'collaboration', 'associate', 'professor', 'menno', 'zelm', 'from', 'monash', 'university', 'published', 'monday', 'preprint', 'server', 'medrxiv']
['researchers', 'found', 'specific', 'cell', 'within', 'human', 'immune', 'system', 'memory', 'cell', 'remembers', 'infection', 'virus', 'challenged', 'again', 'through', 'exposure', 'virus', 'triggers', 'protective', 'immune', 'response', 'through', 'rapid', 'production', 'protective', 'antibodies']
['researchers', 'recruited', 'covid', 'patients', 'took', 'blood', 'samples', 'between', 'four', 'post', 'infection', 'post', 'infection']
['with', 'other', 'studies', 'looking', 'antibody', 'response', 'researchers', 'found', 'that', 'antibodies', 'against', 'virus', 'started', 'drop', 'after', 'days', 'post', 'infection']
['however', 'patients', 'continued', 'have', 'memory', 'cells', 'that', 'recognised', 'components', 'virus', 'spike', 'nucleocapsid', 'proteins']
['these', 'virus', 'specific', 'memory', 'cells', 'were', 'present', 'long', 'eight', 'months', 'after', 'infection']
['associate', 'professor', 'zelm', 'said', 'results', 'gave', 'hope', 'efficacy', 'vaccine', 'against', 'virus', 'explained', 'there', 'been', 'examples', 'genuine', 'reinfection', 'millions', 'people', 'tested', 'positive', 'virus', 'globally']
['these', 'results', 'important', 'because', 'they', 'show', 'definitively', 'that', 'patients', 'infected', 'with', 'covid', 'virus', 'fact', 'retain', 'immunity', 'against', 'virus', 'disease', 'said']
['this', 'been', 'black', 'cloud', 'hanging', 'over', 'potential', 'protection', 'that', 'could', 'provided', 'covid', 'vaccine', 'gives', 'real', 'hope', 'that', 'once', 'vaccine', 'vaccines', 'developed', 'they', 'will', 'provide', 'long', 'term', 'protection']
['newspaper']
['origins', 'covid']
['reuters']
['published', 'january', '2021', 'updated', 'january', '2021']
['shanghai', 'reuters', 'team', 'from', 'world', 'health', 'organization', 'works', 'china', 'investigate', 'origins', 'covid', 'following', 'factbox', 'looks', 'what', 'know', 'about', 'pandemic', 'began']
['china', 'origins']
['coronavirus', 'that', 'causes', 'covid', 'known', 'sars', 'first', 'identified', 'central', 'chinese', 'city', 'wuhan', 'january', '2020', 'most', 'scientists', 'believe', 'still', 'most', 'likely', 'that', 'originated', 'china']
['peter', 'embarek', 'expert', 'food', 'safety', 'zoonotic', 'diseases', 'said', 'wuhan', 'offered', 'first', 'solid', 'clues', 'about', 'transmission', 'covid', 'investigation', 'would', 'start', 'there']
['though', 'some', 'studies', 'have', 'suggested', 'covid', 'present', 'italy', 'spain', 'earlier', '2019', 'that', 'might', 'have', 'been', 'responsible', 'spike', 'pneumonia', 'cases', 'france', 'researchers', 'believe', 'could', 'have', 'entered', 'wuhan', 'from', 'europe']
['closest', 'relative', 'nature', 'ratg13', 'virus', 'which', 'discovered', 'horseshoe', 'bats', 'southwest', 'china', 'yunnan', 'province', 'genetic', 'match', 'between', 'makes', 'highly', 'likely', 'that', 'covid', 'also', 'originated', 'colonies', 'china', 'southwest', 'border', 'regions']
['huanan', 'market']
['initial', 'cluster', 'infections', 'traced', 'back', 'huanan', 'seafood', 'market', 'wuhan', 'leading', 'many', 'assume', 'that', 'patient', 'zero', 'probably', 'trader', 'exposed', 'contaminated', 'meat', 'products']
['this', 'version', 'events', 'simplistic', 'explain', 'patterns', 'infection', 'wuhan', 'elsewhere']
['many', 'earliest', 'reported', 'cases', 'connection', 'with', 'market', '2019', 'wuhan', 'residents', 'were', 'hospitalised', 'with', 'what', 'turned', 'covid', 'them', 'link', 'huanan']
['team', 'chinese', 'researchers', 'said', 'there', 'were', 'types', 'sars', 'circulating', 'wuhan', 'only', 'which', 'associated', 'with', 'market']
['scientists', 'also', 'said', 'virus', 'unusually', 'adapted', 'rapid', 'human', 'transmission', 'making', 'unlikely', 'that', 'first', 'human', 'contact', 'made', 'seafood', 'market']
['intermediary', 'species']
['investigators', 'keen', 'find', 'what', 'intermediary', 'species', 'that', 'allowed', 'sars', 'move', 'from', 'original', 'host', 'into', 'humans']
['preliminary', 'scientific', 'papers', 'china', 'identified', 'snakes', 'mink', 'potential', 'candidates', 'similar', 'coronavirus', 'infections', 'were', 'also', 'found', 'pangolins', 'illegally', 'trafficked', 'into', 'china']
['some', 'scholars', 'believe', 'there', 'intermediary', 'species', 'that', 'sars', 'like', 'virus', 'transmitted', 'directly', 'from', 'bats', 'humans', 'possibly', 'multiple', 'occasions']
['first', 'people', 'infected', 'were', 'likely', 'traders', 'meat', 'droppings', 'used', 'traditional', 'chinese', 'medicine', 'them', 'could', 'have', 'carried', 'into', 'huanan', 'seafood', 'market', 'causing', 'superspreader', 'event', 'that', 'allowed', 'pandemic', 'begin']
['gain', 'function']
['though', 'there', 'credible', 'supporting', 'evidence', 'some', 'researchers', 'still', 'rule', 'possibility', 'that', 'virus', 'released', 'accidentally', 'specialist', 'wuhan', 'institute', 'virology']
['there', 'indication', 'that', 'sars', 'contains', 'synthetic', 'insertions', 'some', 'researchers', 'could', 'have', 'been', 'subject', 'process', 'known', 'gain', 'function', 'where', 'forces', 'virus', 'become', 'more', 'infectious', 'exposing', 'human', 'receptor', 'cells']
['some', 'experts', 'investigators', 'needs', 'granted', 'access', 'research', 'conducted', 'whether', 'there', 'closer', 'matches', 'than', 'ratg13', 'virus']
['there', 'another', 'explanation', 'coronavirus', 'proven', 'lethal']
['sars', 'been', 'circulating', 'humans', 'throughout', 'southwest', 'china', 'months', 'before', 'finally', 'identified', 'wuhan', 'natural', 'selection', 'itself', 'could', 'have', 'trained', 'bind', 'more', 'effectively', 'those', 'receptor', 'cells']
['fully', 'optimised', 'after', 'months', 'human', 'exposure', 'made', 'explosive', 'breakthrough', 'huanan', 'market', 'where', 'conditions', 'were', 'ideal', 'rapid', 'viral', 'transmission', 'though', 'also', 'very', 'likely', 'spreading', 'elsewhere', 'reporting', 'david', 'stanway', 'editing', 'gerry', 'doyle']
['newspaper']
['australian', 'covid', 'testing', 'criteria']
['australian', 'associated', 'press']
['published', 'april', '2020', 'updated', 'april', '2020']
['what', 'criteria', 'people', 'australia', 'need', 'meet', 'order', 'tested', 'covid']
['victoria']
['testing', 'expanded', 'victoria', 'include', 'anyone', 'experiencing', 'fever', 'acute', 'respiratory', 'symptoms']
['testing', 'available', 'anyone', 'recently', 'returned', 'from', 'travel', 'come', 'into', 'contact', 'with', 'confirmed', 'case']
['public', 'screening', 'clinics', 'have', 'opened', 'test', 'people', 'covid']
[]
['south', 'wales']
['testing', 'recommended', 'anyone', 'displaying', 'fever', 'acute', 'respiratory', 'infection', 'symptoms']
['overseas', 'travellers', 'past', 'days', 'cruise', 'ship', 'passengers', 'crew', 'members', 'those', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'case', 'urged', 'tested']
['sick', 'healthcare', 'aged', 'care', 'workers', 'recommended', 'tested', 'infection']
['people', 'living', 'penrith', 'sydney', 'inner', 'west', 'liverpool', 'randwick', 'waverley', 'woollahra', 'blacktown', 'cumberland', 'westmead', 'ryde', 'manning', 'lake', 'macquarie', 'urged', 'seek', 'testing', 'they', 'display', 'symptoms', 'community', 'community', 'transmission', 'occurred', 'these', 'areas']
[]
['queensland']
['people', 'displaying', 'fever', 'acute', 'respiratory', 'symptoms', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'case', 'have', 'been', 'overseas', 'within', 'past', 'days', 'tested']
['workers', 'vulnerable', 'setting', 'including', 'healthcare', 'aged', 'care', 'military', 'education', 'corrections', 'tested']
['people', 'live', 'brisbane', 'gold', 'coast', 'cairns', 'first', 'nations', 'community', 'also', 'tested', 'they', 'show', 'symptoms']
['people', 'travelling', 'from', 'declared', 'covid', 'hotspot', 'interstate', 'also', 'tested']
[]
['south', 'australia']
['residents', 'advised', 'tested', 'they', 'have', 'travelled', 'overseas', 'interstate', 'past', 'days']
['testing', 'advised', 'people', 'have', 'been', 'contact', 'with', 'confirmed', 'case', 'displaying', 'coronavirus', 'symptoms']
['healthcare', 'workers', 'with', 'direct', 'patient', 'contact', 'aged', 'residential', 'care', 'workers', 'have', 'fever', 'acute', 'respiratory', 'infection', 'shortness', 'breath', 'cough', 'sore', 'throat', 'urged', 'tested']
['people', 'tanunda', 'nuriootpa', 'williamstown', 'angaston', 'lyndoch', 'from', 'march', 'have', 'developed', 'covid', 'symptoms', 'should', 'immediately', 'self', 'isolate', 'tested', 'after', 'cases', 'were', 'linked', 'barossa', 'region']
['health', 'recommends', 'testing', 'qantas', 'staff', 'have', 'been', 'affected', 'areas', 'adelaide', 'airport', 'after', 'outbreak', 'cases', 'public', 'areas', 'have', 'been', 'affected', 'anyone', 'been', 'airport', 'developed', 'symptoms', 'urged', 'self', 'isolate', 'seek', 'testing']
[]
['western', 'australia']
['people', 'tested', 'they', 'have', 'fever', 'recent', 'history', 'fever', 'acute', 'respiratory', 'infection', 'shortness', 'breath', 'cough', 'sore', 'throat']
['people', 'working', 'high', 'risk', 'settings', 'such', 'healthcare', 'aged', 'care', 'disability', 'police', 'officers', 'tested']
['residents', 'required', 'have', 'travelled', 'interstate', 'overseas', 'been', 'contact', 'with', 'confirmed', 'case', 'test']
['people', 'metropolitan', 'areas', 'tested', 'nine', 'testing', 'clinics', 'while', 'those', 'regional', 'areas', 'tested', 'public', 'hospital', 'health', 'service', 'remote', 'health', 'clinic']
['authorities', 'indicate', 'more', 'random', 'testing', 'find', 'symptomatic', 'carriers', 'likely', 'next', 'step', 'after', 'very', 'little', 'community', 'spread', 'found', 'state', 'results']
[]
['tasmania', 'tasmanians', 'display', 'coronavirus', 'symptoms', 'have', 'recently', 'travelled', 'overseas', 'interstate', 'apply', 'their', 'through', 'public', 'health', 'services', 'tested']
['people', 'have', 'been', 'cruise', 'ship', 'have', 'been', 'contact', 'with', 'person', 'with', 'covid', 'apply', 'tested']
['state', 'government', 'changed', 'testing', 'criteria', 'allow', 'testing', 'people', 'have', 'spent', 'time', 'tasmania', 'northwest', 'have', 'developed', 'fever', 'respiratory', 'symptoms']
['northern', 'territory', 'people', 'have', 'returned', 'from', 'interstate', 'overseas', 'past', 'days', 'develop', 'respiratory', 'illness', 'with', 'without', 'fever', 'tested']
['people', 'have', 'been', 'close', 'contact', 'with', 'confirmed', 'covid', 'case', 'past', 'days', 'develop', 'respiratory', 'illness', 'with', 'without', 'fever', 'tested']
['people', 'have', 'severe', 'community', 'acquired', 'pneumonia', 'there', 'clear', 'cause', 'seek', 'testing']
['healthcare', 'workers', 'directly', 'work', 'with', 'patients', 'frontline', 'workers', 'including', 'police', 'emergency', 'workers', 'educators', 'retail', 'pharmacists', 'disability', 'workers', 'display', 'respiratory', 'illness', 'fever', 'tested']
[]
['people', 'have', 'recently', 'travelled', 'overseas', 'cruise', 'ship', 'have', 'been', 'contact', 'with', 'confirmed', 'case', 'have', 'developed', 'symptoms', 'covid', 'within', 'days', 'returning', 'australia', 'urged', 'tested']
['healthcare', 'aged', 'care', 'workers', 'with', 'recent', 'onset', 'respiratory', 'symptoms', 'fever', 'irrespective', 'travel', 'history', 'tested']
['people', 'living', 'high', 'risk', 'setting', 'such', 'aged', 'care', 'military', 'correctional', 'facilities', 'have', 'symptoms', 'covid', 'should', 'tested']
['anyone', 'travelled', 'from', 'area', 'where', 'covid', 'outbreaks', 'have', 'occurred', 'have', 'travelled', 'area', 'within', 'australia', 'with', 'elevated', 'risk', 'community', 'transmission', 'displaying', 'symptoms', 'urged', 'tested']
['newspaper']
['auckland', 'removes', 'covid', 'restrictions']
['australian', 'associated', 'press']
['published', 'march', '2021', 'updated', 'march', '2021']
['zealand', 'downscaled', 'covid', 'alert', 'levels', 'auckland', 'marking', 'restrictions', 'from', 'valentine', 'outbreak']
['however', 'jacinda', 'ardern', 'government', 'been', 'criticised', 'making', 'decision', 'thursday', 'delaying', 'announcement']
['first', 'bloke', 'clarke', 'gayford', 'jumped', 'prime', 'minister', 'tweeting', 'government', 'decision', 'four', 'hours', 'before', 'ardern', 'scheduled', 'press', 'conference']
['inprinciple', 'decision', 'decided', 'pending', 'final', 'test', 'results', 'today', 'wrote', 'response', 'criticism', 'from', 'kiwi', 'chef', 'martin', 'bosley']
['feeling', 'really', 'sorry', 'auckland', 'hospo', 'having', 'wait', 'until', '30am', 'today', 'find', 'about', 'moving', 'levels', 'utterly', 'ludicrous', 'stay', 'strong', 'friends', 'bosley', 'said']
['aucklanders', 'were', 'expecting', 'shift', 'there', 'have', 'been', 'community', 'cases', 'fortnight']
['30am', 'ardern', 'announced', 'biggest', 'city', 'would', 'move', 'from', 'alert', 'level', 'alert', 'level', '12noon']
['once', 'again', 'city', 'stepped', 'what', 'needed', 'that', 'thank', 'said']
['look', 'after', 'each', 'other', 'enjoy', 'your', 'weekend']
['decision', 'allows', 'crowds', 'gather', 'watch', 'america', 'sailing', 'auckland', 'racing', 'meet', 'saturday', 'postponed', 'auckland', 'pride', 'events']
['business', 'hospitality', 'figures', 'attacked', 'decision', 'saying', 'allow', 'them', 'enough', 'time', 'change', 'staffing', 'order', 'supplies', 'bumper', 'friday', 'night', 'trade']
['auckland', 'pride', 'director', 'tweedie', 'said', 'wait', 'frustrating']
['sector', 'anticipated', 'move', 'tonight', 'which', 'changes', 'everything', 'friday', 'night', 'shows', 'events', 'said']
['need', 'time', 'roll', 'plans', 'release', 'tickets', 'change', 'seating', 'signage', 'didn']
['opposition', 'leader', 'judith', 'collins', 'called', 'communications', 'good', 'enough']
['ardern', 'said', 'cabinet', 'thursday', 'afternoon', 'decided', 'move', 'alert', 'levels', 'pending', 'cases', 'testing', 'close', 'contacts', 'information', 'that', 'friday', 'morning']
['ardern', 'rejected', 'notion', 'gayford', 'tweet', 'improper']
['have', 'seen', 'partner', 'since', 'yesterday', 'really', 'reject', 'implication', 'said']
['known', 'decision', 'known', 'process', 'which', 'exactly', 'what', 'ministers', 'have', 'conveyed', 'public', 'domain']
['valentine', 'outbreak', 'first', 'identified', 'february', 'prompted', 'government', 'into', 'lockdowns', 'zealand', 'biggest', 'city']
['first', 'three', 'lockdown', 'beginning', 'february']
['week', 'long', 'lockdown', 'began', 'february', 'when', 'community', 'case', 'identified', 'without', 'links', 'cluster', 'subsequently', 'traced', 'back', 'outbreak']
['total', 'people', 'have', 'tested', 'positive', 'covid', 'from', 'latest', 'outbreak', 'health', 'officials', 'discover', 'origin']


# LSA Model
number_of_topics=100
words=word_count_nscience
model_nscience=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_non_science =dict(model_nscience.show_topic(0, topn=words))##dict and encoding matrix values


#dictionary of top 5 words
dict(model_nscience.show_topic(0, topn=5))

{'trial': -0.4357698515176407,
 'vaccin': -0.3522508419172812,
 'stage': -0.3104392334067969,
 'late': -0.27993679585081893,
 'expect': -0.2175291307730242}


#wordcloud for top 10 words
from wordcloud import WordCloud
text = dict(model_nscience.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph11.png')


# Cosinesimilarity matrix of top 5 words

a=pd.DataFrame()

for i in range(number_of_topics):
    words_non_science =dict(model_nscience.show_topic(i, topn=words))
    b=pd.DataFrame(words_non_science,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_nscience.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled13.png')

cosine-similarity
           trial    vaccin     stage      late    expect
trial          1  0.126472  0.861164  0.709278  0.825116
vaccin  0.126472         1  0.179982  0.149438  0.162041
stage   0.861164  0.179982         1  0.838425  0.944611
late    0.709278  0.149438  0.838425         1  0.791853
expect  0.825116  0.162041  0.944611  0.791853         1


#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='y',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig13.png')


#loading dataset
document_list,titles,word_count_psy=load_data("","covid-psychology-small.txt")

Number of words in text file : 39735
Total Number of Documents: 4481


#data preprocessing
clean_text=preprocess_data(document_list)

['international', 'sociology']
['2021']
['author', '2020']
['article', 'reuse', 'guidelines']
['sagepub', 'journals', 'permissions']
['1177', '0268580920948807']
['journals', 'sagepub', 'home']
['psychology', 'politics']
['covid', 'misinfodemics']
['people']
['believe', 'misinfodemics']
['sonia', 'mukhtar']
['university', 'management', 'technology', 'lahore', 'pakistan']
['abstract']
['misinfodemics', 'related', 'covid', 'have', 'negatively', 'impacted', 'people', 'lives', 'with', 'adverse']
['health', 'psycho', 'sociopolitical', 'outcomes', 'scientific', 'community', 'seeks', 'communicate']
['evidence', 'based', 'information', 'regarding', 'misplaced', 'preventive', 'strategies', 'misinformed', 'helpseeking', 'behaviors', 'global', 'multifaceted', 'systems', 'secondary', 'risk', 'emerged', 'effects']
['misinfodemics', 'public', 'published', 'articles', 'pubmed', 'embase', 'google', 'scholar', 'elsevier']
['about', 'covid', 'related', 'misinfodemics', 'have', 'been', 'considered', 'reviewed', 'this', 'article', 'this']
['review', 'examines', 'mechanisms', 'operational', 'structure', 'prevalence', 'predictive', 'factors', 'effects']
['responses', 'potential', 'curtailing', 'strategies', 'misinfodemics', 'covid', 'present', 'article']
['shows', 'that', 'popular', 'variants', 'covid', 'misinfodemics', 'could', 'joint', 'product']
['psychological', 'predisposition', 'which', 'either', 'reject', 'information', 'from', 'experts', 'perceive']
['crisis', 'situation', 'product', 'misinfodemics', 'mechanisms', 'partisan', 'ideological', 'motivations']
['psychological', 'foundations', 'political', 'disposition', 'misinfodemics', 'have', 'implications']
['development', 'strategies', 'designed', 'curtail', 'negative', 'consequences', 'public', 'health']
['keywords']
['conspiracy', 'theories', 'covid', 'health', 'communication', 'misinformation', 'mistrust', 'science']
['mechanisms', 'misinfodemics']
['this', 'modern', 'mainstream', 'social', 'media', 'have', 'become', 'primary', 'source', 'information', 'people', 'around', 'world', 'thus', 'risk', 'misinfodemics', 'surrounding']
['corresponding', 'author']
['sonia', 'mukhtar', 'university', 'management', 'technology', 'block', 'phase', 'johar', 'town', 'lahore']
['punjab', '54770', 'pakistan']
['email', 'sonia', 'mukhtar12', 'gmail']
['948807iss0010', '1177', '0268580920948807international', 'sociologymukhtar']
['research', 'article2020']
['article']
['international', 'sociology']
['covid', 'pandemic', 'even', 'more', 'challenging', 'curb', 'ongoing', 'coronavirus']
['covid', 'pandemic', 'outbreak', 'highlighted', 'interconnectedness', 'modern']
['globalized', 'world', 'where', 'public', 'health', 'threats', 'extend', 'beyond', 'their', 'point', 'origin']
['unvarying', 'reliance', 'misplaced', 'confidence', 'media', 'platforms', 'during']
['lockdown', 'quarantine', 'self', 'isolation', 'social', 'distancing', 'virtual', 'communication']
['become', 'major', 'source', 'interaction', 'holman', '2020']
['covid', 'pandemic', 'outbreak', 'only', 'escalated', 'challenges']
['healthcare', 'social', 'educational', 'economic', 'political', 'environmental', 'cultural', 'socioeconomic', 'systems', 'over', 'world', 'also', 'gained', 'momentum', 'innumerable']
['misinfodemics', 'mechanisms', 'rumors', 'myths', 'superstitions', 'conspiracy', 'theories']
['claims', 'hoaxes', 'false', 'misinformation', 'fake', 'news', 'polarization', 'mistrust', 'science']
['times', 'crisis', 'absence', 'fact', 'checking', 'misinformation', 'misleading', 'content', 'false']
['context', 'manipulated', 'data', 'fabricated', 'material', 'imposter', 'documents', 'disinformation', 'regarding', 'etiology', 'outcomes', 'misplaced', 'prevention', 'disease']
['mukhtar', '2020a', '2020']
['covid', 'pandemic', 'outbreak', 'saturated', 'mainstream', 'media', 'which', 'disseminate', 'information', 'local', 'global', 'scale', 'similarly', 'social', 'media', 'platforms', 'have']
['also', 'become', 'accessible', 'source', 'information', 'numerous', 'incidents', 'initiated']
['these', 'rumors', 'have', 'caused', 'several', 'mishaps', 'across', 'world', 'instance', 'took']
['life', 'after', 'positive', 'diagnosis', 'covid', 'india', 'people', 'have', 'overdosed']
['drug', 'cholorquine', 'after', 'news', 'about', 'effectiveness', 'against', 'covid', 'proliferated', 'nigeria', 'busari', 'adebayo', '2020', '2020', 'there', 'been', 'failure', 'make']
['distinction', 'between', 'empirically', 'based', 'scientific', 'truths', 'fabricated', 'unconfirmed']
['anti', 'science', 'conspiracy', 'theories', 'among', 'general', 'public', 'such', 'reports', 'reduce', 'legitimacy', 'scientific', 'discoveries', 'regarding', 'cure', 'vaccine', 'covid', 'this']
['also', 'create', 'social', 'stigma', 'resulting', 'xenophobia', 'anti', 'chinese', 'sentiment', 'racism', 'marginalization', 'reduced', 'compliance', 'adherence', 'quarantine', 'have', 'adverse', 'health']
['psychosocial', 'impacts', 'aguilera', '2020', 'rana', '2020a', '2020b', 'these', 'considerations', 'become', 'even', 'more', 'exacerbated', 'during', 'lockdown', 'leading', 'people', 'fringe']
['popular', 'opinion', 'spend', 'ever', 'more', 'time', 'social', 'media', 'people', 'trying']
['make', 'sense', 'their', 'changed', 'lives', 'self', 'proclaimed', 'celebrities', 'self', 'identified', 'social']
['media', 'stars', 'politicians', 'mainstream', 'media', 'public', 'figures', 'propagating', 'their']
['subjective', 'interpretations', 'events', 'this', 'situation', 'covid', 'pandemic', 'misinfodemics', 'lederer', '2020']
['scientific', 'studies', 'from', 'previous', 'disease', 'outbreaks', 'have', 'demonstrated', 'that', 'misinformation', 'represents', 'secondary', 'challenge', 'public', 'health', 'efforts', 'controlling', 'epidemic', 'pandemic', 'earnshaw', '2019', 'kalichman', '2009', 'individuals', 'endorse']
['misinformation', 'about', 'disease', 'less', 'likely', 'follow', 'public', 'health', 'instructions', 'during']
['ebola', 'outbreak', '2014', 'respondents', 'gave', 'credence', 'conspiracy', 'theories']
['alleged', 'that', 'they', 'would', 'less', 'likely', 'seek', 'support', 'during', 'disease', 'outbreak']
['national', 'cancer', 'institute', '2020', '2019', 'study', 'montanaro', '2020', 'conducted']
['over', 'respondents', 'reported', 'trust', 'medical', 'healthcare', 'professionals', 'contrast', '2020', 'study', 'where', 'majority', 'respondents', 'reported', 'mistrust', 'information']
['about', 'covid', 'from', 'current', 'administration', 'mainstream', 'media', 'news', 'outlets']
['york', 'times', '2020']
['mukhtar']
['psychology', 'misinfodemics']
['fear', 'adaptive', 'emotion', 'which', 'serves', 'mobilize', 'energy', 'towards', 'potential', 'actual']
['perceived', 'threat', 'mertens', '2018', 'however', 'when', 'fear', 'excessive', 'then', 'this']
['have', 'detrimental', 'effects', 'individual', 'mental', 'health', 'problems', 'like', 'anxiety']
['phobia', 'community', 'level', 'hoarding', 'panic', 'shopping', 'xenophobia', 'when', 'fear']
['insufficient', 'then', 'this', 'also', 'cause', 'harm', 'both', 'individual', 'incompliance', 'nonadherence', 'towards', 'quarantine', 'isolation', 'community', 'reckless', 'implementation']
['policies', 'that', 'ignores', 'risks', 'socioeconomic', 'infrastructure', 'likewise', 'interpersonal']
['safety', 'measures', 'mitigate', 'certain', 'threats', 'transmission', 'disease', 'paradoxically', 'enhance', 'fear', 'fear', 'transmission', 'excessive', 'health', 'anxiety', 'psychosocial']
['issues', 'similarly', 'social', 'safety', 'measures', 'lockdowns', 'curfews', 'help', 'control', 'transmission', 'prolonged', 'stringent', 'measures', 'could', 'have', 'negative', 'consequences', 'socioeconomic', 'impact', 'mental', 'health', 'impact']
['efforts', 'curb', 'misinfodemics', 'process', 'have', 'increased', 'because', 'adverse']
['effects', 'public', 'health', 'communication', 'encourage', 'adoption', 'sustainable']
['preventive', 'measures', 'manage', 'social', 'physical', 'distancing', 'psychological', 'health']
['resilience', 'socioeconomic', 'conditions', 'address', 'stigma', 'prejudice', 'discrimination']
['inequalities', 'mukhtar', '2020b', 'among', 'various', 'factors', 'psychological', 'vulnerability', 'mukhtar', '2020c', 'mukhtar', 'mahmood', '2018', 'propensity', 'follow', 'popular']
['opinion', 'excessive', 'social', 'media', 'presence', 'predisposition', 'anxiety', 'fear', 'lead']
['uncertainty', 'intolerance', 'unpredictability', 'coronavirus', 'brings', 'high', 'levels', 'uncertainty', 'inability', 'cope', 'with', 'uncertainty', 'exponentially', 'higher', 'causing', 'anxiety']
['fear', 'health', 'related', 'worry', 'this', 'situation', 'exacerbated', 'exposure', 'plethora']
['information', 'including', 'misinformation', 'disinformation', 'about', 'impending']
['threat', 'from', 'mainstream', 'media', 'social', 'media', 'alike', 'rosser', '2019', 'perceived', 'threat']
['information', 'elevates', 'fear', 'repeated', 'engagement', 'with', 'trauma', 'related', 'media', 'content']
['several', 'hours', 'daily', 'culminates', 'acute', 'stress', 'emotional', 'distress', 'consequently']
['will', 'either', 'increase', 'fear', 'virus', 'cause', 'insensitivity', 'towards', 'course']
['identification', 'monitoring', 'internet', 'centrifugal', 'clarification', 'filter']
['accuracy', 'content', 'become', 'more', 'challenging', 'increasing', 'number', 'people', 'rely']
['social', 'media', 'platforms', 'challenging', 'problems', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'social', 'media', 'they', 'emulsify', 'into', 'false']
['misleading', 'click', 'bait', 'content', 'world', 'health', 'organization', 'launched']
['mythbuster', 'feature', 'website', 'countermeasure', 'spread', 'unauthentic']
['news', 'social', 'media', 'people', 'reported', 'highest', 'media', 'exposure', 'reported', 'higher']
['acute', 'stress', 'holman', '2020', 'media', 'exposure', 'accumulates', 'ever', 'emerging', 'threats']
['repeated', 'exposure', 'these', 'events', 'increase', 'symptoms', 'distress', 'worry', 'fixation']
['herd', 'mentality', 'misplaced', 'conviction', 'make', 'people', 'more', 'vulnerable', 'media', 'exposure', 'distress']
['alongside', 'amount', 'media', 'exposure', 'type', 'content', 'exposure', 'matters']
['well', 'exposure', 'tragic', 'events', 'graphic', 'images', 'conspiracy', 'theories', 'violence', 'could']
['instigate', 'posttraumatic', 'stress', 'fear', 'future', 'which', 'will', 'lead', 'poor', 'personal']
['functioning', 'recent', 'years', 'mainstream', 'media', 'electronic', 'print', 'social', 'media']
['have', 'established', 'themselves', 'beyond', 'government', 'channel', 'global']
['international', 'sociology']
['population', 'developed', 'affinity', 'news', 'channels', 'particular', 'while', 'older']
['population', 'mainly', 'relies', 'more', 'traditional', 'news', 'channels', 'youth', 'look', 'social', 'media']
['such', 'tiktok', 'instagram', 'youtube', 'twitter', 'facebook', 'whatsapp', 'social', 'media']
['have', 'become', 'conduit', 'spreading', 'rumors', 'deliberate', 'misinformation', 'disinformation', 'conspiracy', 'theories', 'personally', 'motivated', 'anecdotes', 'appeal', 'followers']
['attract', 'attention', 'create', 'panic', 'liang', '2020', 'some', 'self', 'proclaimed', 'celebrities', 'media', 'stars', 'have', 'become', 'influential', 'that', 'large', 'number', 'people', 'rely', 'solely']
['their', 'content', 'merchant', '2020', 'some', 'social', 'media', 'posts', 'circulating', 'many']
['platforms', 'endorsed', 'multiple', 'social', 'media', 'account', 'users', 'encourage', 'optimal']
['personal', 'functioning', 'maximum', 'productivity', 'business', 'oriented', 'activities', 'downplaying', 'intensity', 'this', 'traumatic', 'event', 'certain', 'posts', 'like', 'come']
['this', 'quarantine', 'with', 'skill', 'your', 'side', 'hustle', 'started', 'more', 'knowledge', 'then']
['never', 'lacked', 'time', 'lacked', 'discipline', 'circulating', 'various', 'social', 'media', 'accounts']
['prompting', 'people', 'utilize', 'their', 'time', 'learning', 'things', 'skills', 'exacerbate']
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['outcomes', 'harnessing', 'herd', 'followers', 'subscribers', 'media', 'accounts']
['downplaying', 'trauma', 'that', 'others', 'will', 'have', 'devastating', 'effects', 'mental']
['wellbeing']
['lockdown', 'resulting', 'self', 'isolation', 'quarantine', 'social', 'distancing', 'removed']
['from', 'normal', 'leisure', 'time', 'that', 'might', 'utilized', 'improved', 'personal', 'functioning']
['collective', 'traumatic', 'event', 'which', 'poses', 'serious', 'threat', 'people', 'resulted', 'huge']
['loss', 'lives', 'displacement', 'many', 'individuals', 'mukhtar', 'mukhtar', '2020', 'mukhtar']
['rana', '2020', 'covid', 'individual', 'collective', 'traumatic', 'event', 'directly']
['indirectly', 'affected', 'every', 'individual', 'world', 'efforts', 'should', 'directed', 'towards']
['minimizing', 'negative', 'effects', 'this', 'traumatic', 'covid', 'pandemic', 'survivors']
['many', 'people', 'going', 'through', 'interpersonal', 'traumatic', 'events', 'addition', 'collective']
['trauma', 'covid', 'domestic', 'violence', 'gender', 'based', 'violence', 'abuse', 'mukhtar', '2020d']
['financial', 'burden', 'loneliness', 'emotional', 'behavioral', 'problems', 'grief', 'bereavement']
['fear', 'losing', 'family', 'mental', 'health', 'issues', 'physical', 'injuries', 'fatalities']
['isolated', 'people', 'facing', 'psychological', 'issues', 'require', 'trauma', 'focused', 'psychological']
['support', 'mental', 'health', 'care', 'psychological', 'support', 'guidance', 'treatment', 'intervention']
['information', 'such', 'psychological', 'cyber', 'counseling', 'smartphone']
['mukhtar']
['instance', 'mental', 'health', 'hotlines', 'professional', 'help', 'seeking', 'behaviors', 'should']
['encouraged', 'endorsed', 'related', 'barriers', 'like', 'stigma', 'marginalization', 'discrimination', 'shaming', 'phobias', 'should', 'discouraged', 'through', 'government', 'driven', 'programs']
['evidence', 'based', 'treatment', 'models']
['emerging', 'pandemics', 'command', 'intellectual', 'incapacity', 'decision', 'making']
['degree', 'irrationality', 'when', 'bombarded', 'with', 'conflicting', 'opinions', 'conspiracy', 'theories', 'thus', 'empirical', 'interpretation', 'origins', 'impacts', 'result', 'moral', 'judgments']
['based', 'religious', 'cultural', 'beliefs', 'emergence', 'aids', 'garnered', 'plethora']
['moral', 'judgments', 'orbiting', 'around', 'sexual', 'morality', 'substance', 'abuse', 'arrival']
['sars', 'blame', 'directed', 'towards', 'primitive', 'farming', 'practices', 'guangzhou']
['unprecedented', 'covid', 'pandemic', 'welcomed', 'magnitude', 'misinfodemics']
['multitude', 'fronts', 'ophir', '2018', 'richtel', '2020', 'psychological', 'predisposition', 'reject']
['authorized', 'information', 'through', 'denial', 'byproduct', 'deep', 'founded', 'mistrust']
['tendency', 'view', 'major', 'social', 'political', 'events', 'through', 'conspiracies', 'partisan', 'motivations', 'byproduct', 'conspiracy', 'thinking', 'likely', 'explanatory', 'factors', 'understanding', 'believes', 'covid', 'misinfodemics', 'association', 'between']
['conspiracy', 'thinking', 'defense', 'mechanism', 'denial', 'could', 'potential', 'reason']
['refusal', 'corrective', 'actions', 'several', 'health', 'related', 'cases', 'carey', '2020']
['misinfodemics', 'could', 'motivated', 'various', 'factors', 'epistemic', 'desire', 'causal']
['explanation', 'subjective', 'certainty', 'existential', 'desire', 'control', 'security']
['social', 'desire', 'maintain', 'positive', 'image', 'self', 'group', 'douglas', '2017']
['stigmatization', 'labeling', 'scapegoating', 'fear', 'swiftly', 'follow', 'with', 'narrative', 'which']
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['camps', 'losing', 'sense', 'proportion', 'ability', 'mitigate', 'fear', 'reasonable']
['knowledge', 'based', 'measures', 'challenge', 'normalize', 'perceived', 'threat', 'infection']
['mechanism', 'misinfodemics', 'engrossed', 'silos', 'public', 'information', 'which']
['inept', 'limiting', 'risks', 'wald', '2008', 'instance', 'social', 'theme', 'focusing', 'socioeconomic', 'disruption', 'scientific', 'theme', 'focusing', 'medical', 'health', 'risk', 'communication', 'pandemic', 'theme', 'focusing', 'state', 'global', 'response', 'populations', 'most']
['likely', 'affected', 'emerging', 'disease', 'pandemic', 'simultaneous', 'misinfodemics']
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['including', 'older', 'people', 'people', 'with', 'chronic', 'health', 'conditions', 'people', 'with', 'disability']
['mukhtar', '2020e', 'rowlands', '2015']
['politics', 'coronavirus']
['conspiracy', 'theories', 'heavily', 'influenced', 'geopolitics', 'have', 'spread', 'regarding', 'origin']
['scale', 'prevention', 'treatment', 'self', 'diagnosis', 'disease', 'covid', 'being', 'viral']
['bioweapon', 'genetically', 'engineered', 'rogue', 'government', 'with', 'racist', 'genocidal']
['agenda', 'wage', 'economic', 'psychological', 'chinese', 'biological', 'weapon']
['conspiracy', 'theory', 'which', 'spread', 'throughout', 'united', 'states', 'united', 'kingdom']
['india', 'ukraine', 'biological', 'weapon', 'conspiracy', 'theory', 'spread', 'throughout']
['russia', 'iran', 'china', 'philippines', 'venezuela', 'plot', 'muslims', 'plot', 'jews']
['international', 'sociology']
['espionage', 'population', 'control', 'scheme', 'medical', 'misinformation', 'vaccine', 'preexistence', 'cocaine', 'cure', 'african', 'resistance', 'vegetarian', 'immunity', 'methanol']
['other', 'views', 'endorsed', 'presidents', 'governments', 'public', 'figures', 'abound', 'causing']
['misinfodemics', 'incorrect', 'information', 'about', 'virus', 'which', 'poses', 'risks']
['global', 'scale', 'mccarthy', '2020']
['meanwhile', 'covid', 'could', 'positively', 'viewed', 'through', 'partisan', 'lens']
['right', 'wing', 'nationalism', 'pandemonium', 'allegations', 'coronavirus', 'gift']
['politics', 'misinfodemics', 'censorship', 'pathological', 'nationalism', 'multitude']
['governments', 'have', 'introduced', 'policy', 'blame', 'shifting', 'launching', 'their', 'powerrivalry', 'statements', 'revolving', 'around', 'illogical', 'reasoning', 'narrative', 'national']
['immunity', 'towards', 'covid', 'there', 'claims', 'biological', 'weapon']
['against', 'western', 'world', 'chinese', 'experiment', 'gone', 'wrong', 'china', 'equivalent']
['ussr', 'level', 'threat', 'most', 'infamous', 'chinese', 'virus', 'italy', 'accused', 'migrants']
['from', 'africa', 'disease', 'carriers', 'shores', 'france', 'hungary', 'there']
['narration', 'correlation', 'between', 'immigrants', 'coronavirus', 'reminiscent']
['influx', 'migrants', 'from', 'inflicted', 'countries', '2015', 'called', 'border']
['crisis', 'european', 'right', 'wingers', 'declare', 'enemy', 'changed', 'migrants']
['convergence', 'crises', 'aggravates', 'mistrust', 'scientific', 'political']
['economic', 'bodies', 'around', 'world', 'these', 'narratives', 'imply', 'through', 'heavily', 'nuanced']
['differential', 'discourse', 'that', 'their', 'countries', 'immune', 'coronavirus', 'they', 'would']
['have', 'contracted', 'covid', 'could', 'manage', 'effectively', 'only', 'absence']
['immigrants', 'mccarthy', '2020']
['covid', 'provided', 'rich', 'fodder', 'europe', 'nativist', 'populist', 'tune', 'further']
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['frameworks', 'governments', 'thus', 'such', 'obstinate', 'prejudice', 'confrontations']
['ignite', 'globalization', 'nationalism', 'nativism', 'protectionism', 'tariffs', 'closed', 'borders']
['erection', 'walls', 'which', 'will', 'intensify', 'covid', 'pandemic', 'outbreak']
['pakistan', 'research', 'study', 'conducted', 'ipsos', 'revealed', 'that', 'people']
['country', 'believed', 'that', 'performing', 'ablution', 'will', 'keep', 'them', 'protected', 'from', 'transmitting']
['coronavirus', 'others', 'believed', 'that', 'congregation', 'prayers', 'shaking', 'hands', 'cannot']
['infect', 'anyone', 'since', 'sunnah', 'samaa', '2020', 'claims', 'circulating', 'about', 'pakistanis']
['resistance', 'coronavirus', 'were', 'quite', 'prevalent', 'they', 'were', 'backed', 'pakistan', 'relatively', 'mortality', 'rate', 'these', 'rumors', 'argued', 'that', 'pakistan', 'culture', 'religion', 'geographical', 'location', 'climate', 'made', 'pakistanis', 'less', 'vulnerable', 'virus', 'india']
['political', 'activists', 'claimed', 'that', 'drinking', 'urine', 'applying', 'dung', 'body']
['cure', 'coronavirus', 'parliamentarian', 'claimed', 'that', 'saying', 'namaste', 'instead', 'arab']
['greetings', 'prevents', 'contraction', 'coronavirus', 'influential', 'film', 'celebrity', 'claimed']
['that', 'vibrations', 'generated', 'clapping', 'blowing', 'conch', 'shells', 'will', 'kill', 'virus', 'selfproclaimed', 'tiktok', 'media', 'star', 'claim', 'about', 'eating', 'poisonous', 'fruit', 'preventive']
['measure', 'hospitalized', 'people', 'anti', 'coronavirus', 'drug', 'anti', 'coronavirus']
['mattress', 'have', 'been', 'quite', 'prevalent', 'example', 'popular', 'myth', 'various', 'countries']
['that', 'home', 'remedies', 'cure', 'prevent', 'people', 'from', 'contracting', 'coronavirus']
['these', 'rumored', 'remedies', 'that', 'gained', 'traction', 'social', 'media', 'with', 'severe', 'adverse']
['effects', 'involves', 'mixing', 'sodium', 'chlorite', 'solution', 'with', 'citric', 'acid', 'producing', 'chlorine']
['mukhtar']
['dioxide', 'powerful', 'bleaching', 'agent', 'claiming', 'antimicrobial', 'antiviral', 'antibacterial']
['benefits', 'similar', 'phenomena', 'were', 'observed', 'over', 'world', 'which', 'have', 'prolonged', 'health', 'psychosocial', 'economic', 'consequences', 'covid', 'among']
['general', 'public', '2017']
['becomes', 'more', 'difficult', 'limit', 'negative', 'impact', 'misinfodemics', 'especially', 'when']
['partisanship', 'mobilized', 'this', 'effort', 'there', 'could', 'three', 'strategies', 'overcome']
['these', 'negative', 'effects', 'prevention', 'strategies', 'limit', 'spread', 'exposure', 'misinfodemics', 'corrective', 'strategies', 'founded', 'scientific', 'empirical']
['knowledge', 'challenge', 'these', 'predispositions', 'their', 'effects', 'subsequent', 'belief', 'system']
['reduce', 'uncertainty', 'increase', 'perceived', 'control', 'promote', 'self', 'image', 'collaborative', 'strategies', 'corrective', 'strategies', 'efficacious', 'other', 'political', 'social', 'agents']
['activated', 'mobilized', 'override', 'partisan', 'ideological', 'motivations', 'misinfodemics', 'tendencies', 'when', 'politics', 'media', 'promote', 'misinformation', 'likeminded', 'individuals', 'exposed', 'this', 'rhetoric', 'more', 'likely', 'follow', 'elite', 'cues', 'motivated', 'reasoning']
['engage', 'with', 'these', 'ideas', 'cues', 'from', 'partisan', 'elites', 'have', 'potential', 'inflame']
['foster', 'misinfodemics', 'they', 'shape', 'landscape', 'information', 'general', 'public']
['swire', '2017', 'instance', 'outset', 'covid', 'american', 'political', 'administration', 'referred', 'covid', 'hoax', 'rieder', '2020', 'likened', 'pandemic']
['common', 'brooks', '2020', 'coronavirus', 'bioweapon', 'stevenson', '2020', 'this', 'rhetoric', 'likely', 'encourage', 'adoption', 'related', 'beliefs', 'lead', 'likeminded', 'supporters']
['take', 'threat', 'less', 'seriously', 'media', 'outlets', 'including', 'news', 'personalities', 'media', 'figures']
['cast', 'aspersion', 'threat', 'covid', 'questioning', 'hospitals', 'were', 'truly', 'filled', 'with']
['coronavirus', 'infected', 'patients', 'peters', '2020', 'after', 'toll', 'human', 'life', 'became', 'increasingly', 'apparent', 'unassailable', 'change', 'reporting', 'behavior', 'part', 'media']
['political', 'leaders', 'previously', 'explicitly', 'unequivocally', 'trafficked', 'misinfodemics', 'hinted', 'possibility', 'misinformation', 'having', 'actually', 'served', 'correct', 'prevent', 'negative', 'consequences', 'among', 'general', 'public']
['animosity', 'wrapped', 'hatred', 'inside', 'hostility']
['some', 'media', 'channels', 'initially', 'racially', 'labeled', 'biased', 'headlines', 'covid', 'such']
['chinese', 'virus', 'pandemonium', 'china', 'kids', 'stay', 'home', 'china', 'real', 'sick']
['asia', 'chinese', 'coronavirus', 'kung', 'which', 'caused', 'xenophobia', 'misperceptions']
['misled', 'general', 'public', 'witnessed', 'surge', 'anti', 'chinese', 'sentiment', 'racist', 'driven']
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['european', 'countries', 'many', 'chinese', 'customers', 'were', 'refused', 'entrance', 'into', 'restaurants']
['japan', 'south', 'korea', 'vietnam', 'indonesia', 'amnesty', 'international', '2020']
['office', 'united', 'nations', 'high', 'commissioner', 'human', 'rights', 'ohchr']
['issued', 'statement', 'twitter', 'understandable', 'alarmed', 'coronavirus']
['amount', 'fear', 'excuse', 'prejudice', 'discrimination', 'against', 'people', 'asian', 'descent']
['fightracism', 'call', 'hatred', 'support', 'each', 'other', 'this', 'time', 'public', 'health']
['emergency', 'standup4humanrights']
['similar', 'response', 'twitter', 'began', 'jenesuispasunvirus', 'virus', 'after']
['french', 'newspaper', 'front', 'page', 'headline', 'yellow', 'alert', 'ontario', 'human', 'rights']
['international', 'sociology']
['commission', 'ohrc', 'stated', 'that', 'discrimination', 'prohibited', 'under', 'human', 'rights']
['code', 'aguilera', '2020', 'ontario', 'human', 'rights', 'commission', '2020']
['government', 'dilemma']
['time', 'writing', 'government', 'pakistan', 'under', 'leadership', 'prime', 'minister']
['imran', 'khan', 'witnessed', 'sudden', 'setback', 'with', 'number', 'cases', 'steadily']
['increasing', 'their', 'challenge', 'curtail', 'path', 'disease', 'pakistan', 'however', 'there']
['bigger', 'dilemma', 'government', 'pakistan', 'facing', 'partial']
['lockdown', 'lockdown', 'either', 'this', 'message', 'common', 'intention', 'minimize', 'urgency', 'severity', 'pandemic', 'downplay', 'social', 'isolation', 'measures', 'disregard', 'potential', 'crisis', 'health', 'sector', 'while', 'exaggerating', 'impact']
['economy', 'recession', 'that', 'looms', 'horizon', 'overlook', 'mitigating', 'measures', 'necessary', 'stem', 'transmission', 'medical', 'mistrust', 'among', 'public', 'alwan']
['2020', 'prem', '2020', 'religion', 'politics', 'business', 'fundamentally', 'governing']
['aspects', 'pakistan', 'opposed', 'lockdown', 'putting', 'pressure', 'government', 'consequently', 'resulting', 'lifting', 'lockdown', 'pakistan', 'pandemic', 'pandemic', 'hardline', 'clerics', 'hold', 'power', 'override', 'government', 'social', 'distancing']
['instructions', 'place', 'april', '2020', 'dozens', 'well', 'known', 'clerics', 'signed', 'letter']
['warning', 'that', 'government', 'should', 'exempt', 'mosques', 'from', 'shutdown', 'during']
['month', 'ramadan', 'otherwise', 'invite', 'wrath', 'ulterior', 'narrative', 'insinuating']
['political', 'chaos', 'that', 'clerics', 'have', 'unleashed', 'past', 'exercising', 'their', 'religious', 'authority']
['gather', 'loyalists', 'siege', 'state', 'subservient', 'state', 'already', 'signed']
['agreement', 'deferentially', 'promising', 'abide', 'deal', 'begging', 'question']
['charge', 'government', 'during', 'this', 'pandemic', 'crisis', 'government', 'mosques']
['physical', 'mental', 'health', 'social', 'interpersonal', 'factors', 'including', 'intimate', 'partner']
['violence', 'marital', 'rape', 'child', 'abuse', 'domestic', 'violence', 'racism', 'xenophobia', 'dissociation']
['bias', 'prejudice', 'stigmatization', 'marginalization', 'least', 'pakistani', 'clerics']
['mosques', 'concerns', 'during', 'this', 'public', 'crisis', 'clerics', 'have', 'protect', 'their', 'central', 'interests', 'money', 'power', 'millions', 'dollars', 'charitable', 'donations', 'during', 'ramadan', 'benefit', 'pakistan', 'mosques', 'which', 'under', 'state', 'authority', 'clerics', 'often']
['partake', 'political', 'power', 'challenge', 'government', 'despite', 'evidence', 'prevalent', 'rhetoric', 'undermining', 'covid', 'related', 'risks', 'been', 'endorsed', 'austerely']
['followed', 'public', 'highest', 'level', 'authority', 'pakistan', 'clerics']
['religious', 'misinfodemics', 'branch', 'clerics', 'superciliously', 'propagated', 'doctrine', 'narrated', 'through', 'lens', 'pandemic', 'religious', 'explanation', 'could', 'have']
['allowed', 'such', 'disease', 'emerge', 'only', 'through', 'rigorous', 'devotion', 'congregational']
['prayer', 'followers', 'faith', 'conquer', 'devil', 'machinations', 'form', 'coronavirus', 'anyone', 'tries', 'halt', 'these', 'efforts', 'then', 'these', 'zionist', 'agents', 'pawns']
['evil', 'bent', 'destroying', 'faith', 'this', 'dogmatic', 'doctrine', 'seeps', 'through', 'ever', 'present']
['ever', 'omniscient', 'online', 'media', 'infiltrate', 'minds', 'sheeple', 'votary', 'conformists', 'usually', 'heads', 'household', 'enforce', 'these', 'maladaptive', 'cognitive', 'twisted', 'patterns']
['within', 'their', 'families', 'these', 'instructions', 'dictated', 'higher', 'authorities', 'their']
['adherence', 'infringing', 'individual', 'rights', 'freedom', 'planting', 'unquestionable']
['canon', 'propagating', 'anti', 'science', 'medical', 'mistrust', 'among', 'public', 'putting', 'people']
['mukhtar']
['risk', 'halting', 'emergency', 'control', 'preventive', 'measures', 'necessary', 'curb']
['pandemic', 'thus', 'creating', 'public', 'panic', 'social', 'unrest', 'distrust', 'systems', 'kalb']
['2003', 'strong', '1990', 'these', 'eschewed', 'beliefs', 'result', 'adverse', 'behavioral', 'consequences', 'instance', 'failing', 'administer', 'polio', 'vaccination', 'children', 'attributing', 'islamic', 'contributing', 'resurgence', 'this', 'once', 'eradicated', 'disease']
['pakistan', 'misinfodemics', 'especially', 'those', 'which', 'revolve', 'around', 'science', 'medicine']
['health', 'related', 'topics', 'widespread', 'prompting', 'people', 'eschew', 'appropriate', 'healthrelated', 'behaviors', 'jolley', 'douglas', '2014', 'oliver', 'wood', '2014']
['potential', 'strategies', 'curtailment']
['research', 'predicted', 'there', 'psychosocial', 'stress', 'adverse', 'health', 'outcomes']
['people', 'self', 'isolation', 'social', 'distancing', 'quarantine', 'this', 'crisis', 'situation', 'will']
['require', 'remediation', 'from', 'credible', 'sources', 'information', 'these', 'include']
['example', 'centers', 'disease', 'control', 'prevention', 'national', 'institutes']
['health', 'especially', 'which', 'partnered', 'with', 'several', 'social', 'media']
['platforms', 'technological', 'companies', 'google', 'linkedin', 'microsoft', 'reddit', 'twitter']
['facebook', 'youtube', 'promote', 'health', 'updates', 'curb', 'misinformation', 'disinformation', 'hossain', '2020', 'they', 'seeking', 'safeguard', 'ensure', 'effective', 'communication', 'covid', 'between', 'healthcare', 'systems', 'general', 'public', 'despite']
['these', 'efforts', 'infodemics', 'rampant', 'multiple', 'misinformation', 'disinformation']
['sources', 'circulating', 'social', 'media', 'accounts', 'address', 'these', 'discrepancies', 'certain']
['strategies', 'implemented', 'empirically', 'evidence', 'based', 'scientific', 'research', 'findings']
['with', 'integration', 'communication', 'information', 'technology', 'frontline', 'healthcare', 'providers', 'communicate', 'with', 'patients', 'caregivers', 'populations', 'risk']
['results', 'positive', 'health', 'outcomes', 'subsequent', 'optimization', 'resources']
['building', 'strategic', 'partnerships', 'local', 'global', 'levels', 'coordinate', 'connecting', 'offline']
['online', 'resources', 'communication', 'uniform', 'information', 'across', 'platforms', 'from', 'mass', 'media', 'community', 'organizations', 'support', 'groups', 'community']
['society', 'contain', 'infodemics', 'information', 'disinformation', 'disseminate']
['scientifically', 'evidence', 'based', 'information', 'through', 'data', 'mining', 'algorithms', 'detect']
['remove', 'fake', 'news', '2017', 'those', 'propagate', 'misinformation']
['accountable', 'individuals', 'online', 'portals', 'should', 'identified', 'local', 'authorities']
['enforcement', 'agencies', 'precautionary', 'culturally', 'tailored', 'information', 'translated', 'into']
['multiple', 'language', 'manuals', 'factual', 'data', 'regarding', 'covid', 'should', 'promoted']
['through', 'mass', 'media', 'campaigns', 'care', 'evidence', 'based', 'approach', 'services']
['local', 'languages', 'mostly', 'graphic', 'pictorial', 'easy', 'understanding', 'should', 'made']
['available', 'people', 'with', 'limited', 'access', 'healthcare', 'elderly', 'people', 'rural', 'areas']
['general', 'public', 'self', 'isolation', 'quarantined', 'decrease', 'covid', 'infection']
['imperative', 'bring', 'communities', 'institutional', 'leadership', 'together', 'promote']
['transparency', 'good', 'information', 'governance', 'control', 'misinfodemics', 'related']
['covid', 'both', 'people', 'physical', 'mental', 'psychosocial', 'health', 'sound', 'economic', 'political', 'systematic', 'functioning', 'during', 'this', 'pandemic', 'oliver', 'wood', '2014']
['world', 'needs', 'coordinated', 'national', 'international', 'efforts', 'apply', 'scientific']
['empirical', 'data', 'local', 'settings', 'mitigate', 'grave', 'predicament', 'lockdown', 'aftereffects']
['international', 'sociology']
['associated', 'issues', 'mass', 'media', 'healthcare', 'organizations', 'community', 'based', 'organizations', 'stakeholders', 'should', 'strategically', 'join', 'partnership', 'disseminate', 'based']
['mutual', 'consensus', 'empirically', 'based', 'public', 'health', 'messages', 'remove', 'anti', 'science']
['online', 'content', 'through', 'natural', 'language', 'processing', 'data', 'mining', 'approaches', 'best']
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['paper']
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[]
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['riphah', 'international', 'university', 'lahore', 'pakistan']
['clinical', 'psychologist', 'amina', 'abdullah', 'hospital', 'deplapur', 'okara', 'pakistan']
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['abstract']
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['keywords', 'covid', 'bibliometric', 'analysis', 'bibliographic', 'coupling', 'psychological']
['interventions']
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['nawaz', 'saeed', 'sajeel', '2020', 'covid', 'state', 'research', 'from', 'perspective']
['psychology', 'international', 'journal', 'business', 'psychology']
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['mobilize', 'study', 'rapid', 'evaluation', 'treatment', 'community', 'level']
['international', 'journal', 'business', 'psychology']
['issue', '1ijbpsy', 'nawaz', '2020']
[]
['evaluate', 'available', 'data', 'immediately', 'find', 'what', 'level', 'treatment', 'strategies']
['most', 'effective', 'from', 'china', 'elsewhere']
['evaluate', 'impact', 'adjunctive', 'supportive', 'therapies', 'fast', 'possible']
['optimize', 'protective', 'equipment', 'other', 'interventions', 'avoid', 'manage']
['infections', 'healthcare', 'community', 'environments']
['review', 'current', 'information', 'classify', 'animal', 'host', 'avoid', 'continued', 'spillover']
['better', 'understand', 'transmissibility', 'virus', 'various', 'contexts', 'over', 'time', 'disease']
['frequency', 'more', 'vulnerable', 'infection']
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['better', 'understand', 'virus', 'transmissibility', 'different', 'contexts', 'over', 'time', 'severity']
['disease', 'more', 'susceptible', 'infection']
['accelerate', 'evaluation', 'investigational', 'therapeutics', 'vaccines', 'using', 'master']
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['genetics', 'molecular', 'biology', 'pharmacology', 'toxicology', 'pharmaceutics', 'however', 'very']
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[]
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[]
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['canada']
['australia']
['italy']
['ireland']
['spain']
['france']
['india']
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['figure', 'countriesijbpsy', 'nawaz', '2020']
[]
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['italy', '4410']
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[]
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['journal', 'affective', 'disorders']
['nature', 'human', 'behaviour']
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[]
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[]
[]
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[]
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[]
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['khurram', 'shehzad1']
['xiaoxing1']
['muhammad', 'arif2']
['khaliq', 'rehman3']
['muhammad', 'ilyas2']
['school', 'economics', 'management', 'southeast', 'university', 'nanjing', 'china', 'school', 'economics', 'finance']
['jiaotong', 'university', 'china', 'school', 'management', 'wuhan', 'university', 'technology', 'wuhan', 'china']
['novel', 'coronavirus', 'covid', 'imperatively', 'shaken', 'behavior', 'global']
['financial', 'markets', 'this', 'study', 'estimated', 'impact', 'covid', 'behavior']
['financial', 'markets', 'europe', 'results', 'revealed', 'that', 'returns']
['index', 'have', 'been', 'greatly', 'affected', 'lockdown', 'owing', 'covid']
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['stock', 'returns', 'ftse', 'stock', 'markets', 'witnessed', 'that', 'short']
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['inferring', 'that', 'sustainable', 'development', 'these', 'nations', 'risk', 'covid']
['study', 'suggested', 'that', 'governments', 'should', 'allocate', 'more', 'their', 'budget', 'health']
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['keywords', 'covid', 'financial', 'stability', 'financial', 'markets', 'psychology', 'sustainable', 'development', 'global']
['development', 'health', 'crisis', 'economic', 'crisis']
['introduction']
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['life', 'also', 'impact', 'within', 'economy', 'their', 'financial', 'markets', 'specific']
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['billion', 'dollars', 'fernandes', '2020', 'however', 'potential', 'damages', 'current']
['virus', 'still', 'largely', 'unknown', 'significantly', 'coronavirus', 'covid', 'infection', 'disease']
['first', 'reported', 'wuhan', 'december', '2019', 'spread', 'rapidly', 'almost']
['whole', 'world', 'within', 'next', 'months', 'albulescu', '2020', 'compared', 'severe', 'acute']
['respiratory', 'syndrome', 'sars', 'covid', 'more', 'contagious', 'which', 'been', 'indicated']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['different', 'fatality', 'rate1']
['covid', 'infected']
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['2020', 'order', 'eradicate', 'this', 'pandemic', 'world', 'health']
['organization', 'recommended', 'maintaining', 'social', 'distance']
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['2020', 'accordingly', 'economic', 'circle', 'whole']
['world', 'been', 'disturbed', 'notably', 'sale', 'online', 'travel']
['agencies', 'otas', 'airlines', 'hotels', 'unexpectedly', 'declined']
['world', 'economic', 'forum', '2020', 'even', 'prices', 'have', 'nosedived']
['sudden', 'outbreak', 'this', 'pandemic', '2020']
['globalization', 'current', 'coronavirus', 'outbreak', 'will', 'aggravate']
['economic', 'condition', 'which', 'pave', 'toward', 'financial']
['meltdown', 'huang', '2020', 'this', 'pandemic', 'caused']
['severe', 'psychological', 'impact', 'economy', 'while', 'agitating']
['service', 'industries', 'financial', 'markets']
['moreover', 'coronavirus', 'outbreak', 'severely', 'affected']
['financial', 'markets', 'while', 'declining', 'value', 'stock', 'index']
['daube', '2020', 'surprisingly', 'news', 'specific', 'events']
['fluctuate', 'stock', 'values', 'shehzad', 'sohail', '2018', 'figure']
['revealed', 'market', 'value', 'european', 'american', 'chinese']
['hong', 'kong', 'markets', 'from', 'january', '2020', 'march', '2020']
['period', 'divided', 'into', 'three', 'rounds', 'comparison', 'purposes']
['first', 'time', 'lagged', 'from', 'january', '2020', 'january', '2020']
['second', 'time', 'lagged', 'from', 'january', 'march']
['third', 'time', 'lagged', 'denoted', 'period', 'march', '2020', 'march']
['2020', 'results', 'showed', 'that', 'market', 'values', 'these']
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['share', 'value', 'decreased', 'third', 'time', 'lagged']
['covid', 'tremendously', 'shrank', 'greece', 'stock', 'markets']
['moreover', 'figure', 'illustrates', 'volatility', 'index', 'from']
['january', '2005', 'march', '2020', 'denotes', 'that', 'world', 'faced']
['severe', 'financial', 'crises', '2007', '2009', 'stock', 'markets', 'collapsed']
['economies', 'went', 'through', 'enormous', 'pressure', 'similar']
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['after', 'global', 'financial', 'crises']
['this', 'regard', 'novel', 'coronavirus', 'impact', 'also']
['detrimental', 'pandemic', 'behavior', 'unknown', 'effect']
['stock', 'return', 'last', 'longer', 'relevant', 'this']
['stock', 'volatility', 'been', 'agitated', 'enormous', 'spread']
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['advanced', 'countries', 'stock', 'return', 'this', 'investigation', 'argued']
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['around', 'globe']
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['index', 'represents']
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['dependent', 'variable', 'confirmed', 'cases', 'deaths']
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['computed', 'follows', 'syllignakis', 'kouretas', '2011', 'shehzad']
['sohail', '2018']
['csti']
[]
['here']
['denote', 'return', 'value', 'time', 'closing']
['price', 'time', 'previous', 'closing', 'price', 'time']
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['1sprt', 'β1usct', 'β2usdt']
['1nsrt', 'β1usct', 'β2usdt']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'deterioration', 'stock', 'markets', 'owing', 'novel', 'coronavirus']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['figure', 'variations', 'global', 'financial', 'crisis', '2007', '2009']
['covid', 'crisis']
['1ibert', 'β1spct', 'β2spdt']
['1ftmrt', 'β1itlct', 'β2itldt']
['1daxrt', 'β1gerct', 'β2gerdt']
['1lsert', 'β1ukct', 'β2ukdt']
['where', 'signifies', 'first', 'difference', 'β0β1β2']
['independent', 'parameters', 'however', 'iber', 'ftmr']
['daxr', 'lser', 'defines', 'return', 'values', 'nasdaq']
['composite', 'index', 'ibex', 'ftse']
['london', 'stock', 'exchange', 'respectively', 'moreover']
['itlc', 'gerc', 'indicate', 'confirmed', 'cases', 'covid', 'spain', 'italy', 'germany', 'respectively']
['further', 'itld', 'gerd', 'refer', 'confirmed']
['deaths', 'covid', 'spain', 'italy', 'germany']
['respectively']
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['study', 'employed', 'linear', 'autoregressive', 'distributed']
['nardl', 'model', 'introduced', 'shin', '2014', 'this', 'model']
['performs', 'best', 'with', 'small', 'number', 'observations']
['applied', 'mixed', 'level', 'stationary', 'data']
['evaluates', 'linearity', 'cointegration', 'between', 'variables']
['equation', 'shared', 'short', 'variations', 'long']
['asymmetries', 'after', 'taking', 'long', 'parameters', 'into', 'account']
['shin', '2014', 'nardl', 'model', 'extended', 'form']
['ardl', 'pesaran', '2001', 'model', 'hence', 'full', 'form']
['nardl', 'model', 'each', 'stock', 'market', 'specified']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
['1usd']
[]
['sprt', 'γ2ausc']
['γ2busc']
['γ3ausd']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['γ1nsrt', 'γ2ausc']
['γ2busc']
[]
[]
['γ3busd']
[]
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spd']
[]
[]
[]
[]
[]
[]
['γ1ibert', 'γ2aspc']
['γ2bspc']
[]
[]
['γ3bspd']
[]
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
['itld']
['γ1ftmrt', 'γ2aitlc']
[]
['itlc']
[]
['itld']
[]
['itld']
[]
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
['gerd']
['γ1daxrt', 'γ2agerc']
['γ2bgerc']
[]
['gerd']
['γ3bgerd']
[]
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukd']
[]
[]
[]
[]
[]
[]
['γ1lsert', 'γ2aukc']
['γ2bukc']
[]
[]
['γ3bukd']
[]
['here', 'ϑ0ϑ1', 'ϑ2bϑ3aand', 'short', 'factors', 'while']
[]
[]
['indicates', 'long', 'parameters']
['whereas', 'symbolizes', 'number', 'used', 'model']
['based', 'akaik', 'information', 'criterion', 'schwarz', 'information']
['criterion', 'above', 'stated', 'equations', 'assumed', 'that']
['confirmed', 'number', 'cases', 'deaths', 'covid', 'have']
['asymmetrical', 'impact', 'stock', 'returns']
['thus']
[]
[]
[]
[]
[]
[]
[]
['itlc']
[]
[]
['itld']
[]
['gerc']
[]
['gerd']
[]
[]
[]
[]
[]
['designates', 'positive', 'shock', 'though']
[]
[]
[]
[]
[]
[]
[]
[]
['itlc']
[]
['itld']
[]
['gerc']
[]
['gerd']
[]
[]
[]
[]
[]
[]
['implies', 'negative', 'shock', 'each', 'variable']
['computed', 'follows']
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
[]
['here']
[]
['delineates', 'positive', 'shock', 'variable', 'denotes']
['asymmetric', 'distributive', 'error', 'correction', 'form']
['these', 'factors', 'written', 'follows']
['1sprt']
[]
[]
[]
['1sprt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1nsrt']
[]
[]
[]
['1nsrt']
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usc']
[]
[]
[]
[]
[]
['1usd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ibert']
[]
[]
[]
['1ibert']
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spc']
[]
[]
[]
[]
[]
['1spd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
['1ftmrt']
[]
[]
[]
['1ftmrt']
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itlc']
[]
[]
[]
[]
[]
['1itld']
[]
[]
[]
[]
[]
['itld']
['φ1ectt']
['1daxrt']
[]
[]
[]
['1daxrt']
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerc']
[]
[]
[]
[]
[]
['1gerd']
[]
[]
[]
[]
[]
['gerd']
['φ1ectt']
['1lsert']
[]
[]
[]
['1lsert']
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukc']
[]
[]
[]
[]
[]
['1ukd']
[]
[]
[]
[]
[]
[]
['φ1ectt']
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['cointegration', 'among', 'variables', 'examined', 'through', 'bound']
['test', 'approach', 'pesaran', '2001', 'this', 'method', 'relies', 'test', 'evaluate', 'null', 'hypothesis']
['this', 'purpose', 'pesaran', '2001', 'defined', 'bounds']
['upper', 'lower', 'bound', 'estimated', 'statistics']
['higher', 'than', 'upper', 'bound', 'limit', 'then', 'null', 'hypothesis']
['cointegration', 'rejected', 'however', 'projected', 'value', 'test', 'lower', 'than', 'lower', 'bound', 'limit', 'then', 'null', 'hypothesis', 'cannot']
['rejected', 'besides', 'value', 'test', 'remains', 'between', 'both']
['limits', 'then', 'results', 'conclusive', 'this', 'project']
['employed', 'wald', 'test', 'verify', 'asymmetric', 'long']
[]
[]
[]
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[]
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[]
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['andshort']
[]
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['asymmetric']
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['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
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['test', 'dickey', 'fuller', '1979', 'phillip', 'perron', 'test']
['phillips', 'perron', '1988', 'showed', 'that', 'study', 'variables']
['have', 'diverse', 'stationary', 'levels', 'variable']
['cointegrated']
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['respectively', 'these', 'values']
['surpass', 'upper', 'bound', 'limits', 'shin', '2014', 'implying']
['that', 'long', 'cointegration', 'exists', 'among', 'economic', 'crisis', 'health']
['crisis', 'financial', 'markets', 'each', 'equation']
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['particularized', 'that', 'have', 'linear', 'association']
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['skewness', '68003', '687765', '641313', '027343', '392073', '081854', '35422', '186996']
['kurtosis', '36907', '166164', '206693', '903565', '778953', '75376', '380136', '389965']
['jarque', 'bera', '7798', '26244', '69524', '87966', '1793', '8589', '47298', '76708']
['probability', '00434', '000161']
['gerc', 'gerd', 'daxr', 'itld', 'itlc', 'ftmr']
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['skewness', '927378', '53985', '120857', '230936', '65869', '232889', '058817', '31595']
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['gerd', '935247', '359161', '147762', '393867']
['ftmr', '631659', '55646', '451458', '1619']
['itld', '127702', '174362', '400067', '35456']
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['ftmr']
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[]
[]
['daxr']
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[]
[]
['iber', 'lser']
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[]
[]
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['health', 'management', 'system', 'have', 'imperative', 'control', 'over']
['financial', 'markets', 'behavior', 'verdicts', 'elaborated', 'that']
['economic', 'crisis', 'produced', 'result', 'lockdown', 'circumstances']
['spain', 'more', 'impact', 'ibex', 'compared']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['table', 'diagnostic', 'test', 'results']
['heteroskedasticity', 'value', 'serial', 'correlation', 'value', 'heteroskedasticity', 'value', 'serial', 'correlation', 'value']
['ftmr']
['statistic', 'statistic', '5464', 'statistic', 'statistic', '3644']
['ramsey', 'reset', 'statistic', '3623', 'ramsey', 'reset', 'statistic', '7672']
['daxr']
['heteroskedasticity', 'serial', 'correlation', 'heteroskedasticity', 'serial', 'correlation']
['statistic', 'statistic', '6362', 'statistic', 'statistic', '8397']
['ramsey', 'reset', 'statistic', '1927', 'ramsey', 'reset', 'statistic', '2731']
['iber', 'lser']
['heteroskedasticity', 'serial', 'correlation', 'heteroskedasticity', 'serial', 'correlation']
['statistic', 'statistic', '4019', 'statistic', 'statistic', '9939']
['ramsey', 'reset', 'statistic', '4698', 'ramsey', 'reset', 'statistic', '3502']
['denote', 'level', 'significance', 'respectively', 'source', 'author', 'calculation']
['figure', 'cusum', 'cusumsq', 'graph']
['health', 'crisis', 'that', 'resulted', 'spain', 'long', 'italy']
['stock', 'markets', 'more', 'affected', 'health', 'crisis', 'than']
['economic', 'crisis', 'while', 'short', 'economic', 'uncertainty']
['also', 'lowered', 'stock', 'returns', 'ftse', 'nevertheless']
['health', 'crisis', 'germany', 'pointing', 'significant', 'upsurge']
['economic', 'crisis', 'intimated', 'unimportant', 'impression']
['ftse', 'index', 'stock', 'markets', 'recorded', 'that']
['short', 'upsurge', 'health', 'crisis', 'imperatively']
['damages', 'stock', 'returns', 'index']
['long', 'this', 'influence', 'left', 'reverse', 'therefore', 'this']
['research', 'authenticated', 'that', 'health', 'crisis', 'begun', 'with', 'covid', 'devised', 'another', 'global', 'financial', 'crisis', 'almost']
['every', 'nation', 'fighting', 'with', 'these', 'crises', 'today', 'moreover']
['study', 'disclosed', 'that', 'economic', 'uncertainty', 'generated']
['covid', 'highest', 'german', 'stock', 'markets', 'while']
['index', 'second', 'ranking', 'italian']
['stock', 'markets', 'third', 'ranking', 'study', 'concluded']
['that', 'health', 'crisis', 'economic', 'crisis', 'ominously', 'affected']
['stock', 'markets', 'globe', 'consequently', 'significant']
['amount', 'should', 'allocated', 'budget', 'explore']
['prevent', 'these', 'pandemics', 'future', 'further', 'complete']
['lockdown', 'strategy', 'does', 'prove', 'excellent', 'remedy']
['harms', 'financial', 'markets', 'other', 'strategies', 'should']
['developed', 'smart', 'partial', 'lockdown', 'these', 'suggestions']
['essential', 'policymakers', 'government', 'officials', 'researchers']
['general', 'public']
['frontiers', 'psychology', 'frontiersin', 'september', '2020', 'volume', 'article', '1924fpsyg', '01924', 'september', '2020', 'time']
['shehzad', 'financial', 'markets', 'behavior', 'covid']
['data', 'availability', 'statement']
['publicly', 'available', 'datasets', 'were', 'analyzed', 'this', 'study', 'this', 'data']
['found', 'here', 'https', 'ecdc', 'europa']
['author', 'contributions']
['conceptualization', 'analysis', 'methodology']
['supervision', 'introduction', 'results', 'discussion']
['conclusion', 'revising', 'work', 'proofreading', 'visualization']
['authors', 'contributed', 'article', 'approved']
['submitted', 'version']


# LSA Model
number_of_topics=100
words=word_count_psy
model_psy=create_gensim_lsa_model(clean_text,number_of_topics,words)
words_from_psychology =dict(model_psy.show_topic(0, topn=words))##dict and encoding matrix values


#dictionary of top 5 words
dict(model_psy.show_topic(0, topn=5))

{'pandem': 0.3326166193080187,
 'polici': 0.3139495133883562,
 'health': 0.27521477458171695,
 'covid': 0.2512038811196319,
 'impact': 0.23725655752332728}


#wordcloud for top 5 words
from wordcloud import WordCloud
text = dict(model_psy.show_topic(0, topn=20))
l=list(text.keys())
wordcloud = WordCloud(width=150, height=200,max_font_size=25, max_words=20, background_color="white").generate(" ".join(l))
plt.imshow(wordcloud, interpolation='bilinear')
plt.axis("off")
plt.savefig('graph29.png')


# cosine similarity matrix for top 5 words
a=pd.DataFrame()

for i in range(number_of_topics):
    words_psychology =dict(model_psy.show_topic(i, topn=words))
    b=pd.DataFrame(words_psychology,index=[i])
    a=a.append(b)

a=a.transpose()

top_words = dict(model_psy.show_topic(0,topn=5))

df = pd.DataFrame(columns=list(top_words.keys()), 
                  index = list(top_words.keys()))

l = list(top_words.keys())
print('cosine-similarity')
for i in l:
    for j in l:
            matrix=np.array([a.loc[i],a.loc[j]])
            n = s.metrics.pairwise.cosine_similarity(matrix, matrix, dense_output=True)
            df.loc[[i],[j]] = n[0,1]
print(df)

dfi.export(df, 'df_styled14.png')

cosine-similarity
             pandem      polici       health        covid       impact
pandem            1 -0.00470168  -0.00359301  0.000337034   0.00558144
polici  -0.00470168           1      0.02514  -0.00434368  -0.00157005
health  -0.00359301     0.02514            1  -0.00213173  0.000864294
covid   0.000337034 -0.00434368  -0.00213173            1   0.00554273
impact   0.00558144 -0.00157005  0.000864294   0.00554273            1


#networkx graph for top 5 words
df = df.apply(pd.to_numeric, errors='coerce')

df=df.round(4)

l = list(top_words.keys())

df_adj = pd.DataFrame(df.to_numpy(), index=l, columns=l)
G = nx.from_pandas_adjacency(df)
pos = {l[0]: (0, 0),l[1]: (1, 0), l[2]: (0, 1), l[3]: (1, 1), l[4]: (0.5, 0.8)}
a=list(G.edges(data=True))

b=[]
for i in a:
    b.append(i[:][2]['weight'])
w = [x *10  for x in b]

colors = range(4)
nx.draw(G,pos, alpha=1, width=w, with_labels = True,node_size=200, edge_color='r',node_color='b')
labels = nx.get_edge_attributes(G,'weight')
nx.draw_networkx_nodes(G, pos, nodelist=l, node_color="w")
nx.draw_networkx_edge_labels(G,pos,edge_labels=labels)
plt.rcParams["figure.figsize"] = (8,5)
plt.savefig('fig14.png')


mantel_test(5)

Pairwise-distance
          pandem       polici    health     covid       impact    model  \
pandem         0     0.805036   1.01123   1.25503     0.809695  1.27776   
polici  0.805036  9.31323e-10  0.722102  0.992408     0.158666   0.9948   
health   1.01123     0.722102         0   1.24213     0.650069  1.23484   
covid    1.25503     0.992408   1.24213         0     0.993882  1.40107   
impact  0.809695     0.158666  0.650069  0.993882  5.26836e-09  1.00463   
model    1.27776       0.9948   1.23484   1.40107      1.00463        0   
imag     1.28264     0.990826   1.23416     1.398      1.00471  1.39962   
use      1.27322     0.991493   1.23268   1.40142      1.00621   1.4032   
detect   1.25514     0.961398   1.19357   1.36915     0.959384  1.37647   

               imag       use    detect  
pandem      1.28264   1.27322   1.25514  
polici     0.990826  0.991493  0.961398  
health      1.23416   1.23268   1.19357  
covid         1.398   1.40142   1.36915  
impact      1.00471   1.00621  0.959384  
model       1.39962    1.4032   1.37647  
imag    1.49012e-08   1.40125    1.3768  
use         1.40125         0   1.37741  
detect       1.3768   1.37741         0  
Pairwise-distance
          pandem    polici    health        covid       impact        model  \
pandem         0   1.17558   1.37708      1.39721       1.3847      1.01878   
polici   1.17558         0    1.1391      1.18201      1.17001     0.759153   
health   1.37708    1.1391         0      1.38173       1.3707      1.01277   
covid    1.39721   1.18201   1.38173  2.10734e-08      1.39031      1.04557   
impact    1.3847   1.17001    1.3707      1.39031  2.98023e-08      1.03056   
model    1.01878  0.759153   1.01277      1.04557      1.03056  5.26836e-09   
imag    0.987712  0.639016  0.960914     0.994026     0.982477     0.314507   
use      1.27861   1.04098   1.26281      1.29385      1.27682     0.776152   
detect  0.984703    0.6405  0.960757     0.993522     0.980479     0.314485   

              imag       use       detect  
pandem    0.987712   1.27861     0.984703  
polici    0.639016   1.04098       0.6405  
health    0.960914   1.26281     0.960757  
covid     0.994026   1.29385     0.993522  
impact    0.982477   1.27682     0.980479  
model     0.314507  0.776152     0.314485  
imag    1.6131e-09  0.820354    0.0686742  
use       0.820354         0     0.819518  
detect   0.0686742  0.819518  6.58545e-10

(-0.36044591617335736, 0.83188, -1.077892663878568)


mantel_test(10)

Pairwise-distance
             pandem       polici    health     covid       impact      2020  \
pandem            0     0.805036   1.01123   1.25503     0.809695   1.22311   
polici     0.805036  9.31323e-10  0.722102  0.992408     0.158666  0.930378   
health      1.01123     0.722102         0   1.24213     0.650069   1.18737   
covid       1.25503     0.992408   1.24213         0     0.993882    1.3589   
impact     0.809695     0.158666  0.650069  0.993882  5.26836e-09  0.948735   
2020        1.22311     0.930378   1.18737    1.3589     0.948735         0   
develop    0.857162     0.282271  0.772437   1.02832     0.288594  0.977722   
countri     0.78996    0.0978683  0.731875  0.997722     0.176283  0.935336   
nation     0.959555     0.502134  0.801066   1.10818     0.484074   1.06107   
sustain    0.802517     0.059464  0.735623   0.99013     0.151684  0.927968   
model       1.27776       0.9948   1.23484   1.40107      1.00463   1.35636   
imag        1.28264     0.990826   1.23416     1.398      1.00471   1.35426   
use         1.27322     0.991493   1.23268   1.40142      1.00621   1.35885   
detect      1.25514     0.961398   1.19357   1.36915     0.959384   1.33614   
data        1.27319     0.977506   1.21628   1.38877     0.979238   1.35008   
class        1.2708     0.986404   1.22612   1.39319     0.993789    1.3532   
pneumonia   1.26171     0.944239   1.19465    1.3651     0.962037   1.32371   
learn       1.25492     0.971011    1.2104   1.38827     0.961697   1.34298   
dataset     1.27099     0.987866   1.23441   1.39641     0.998986    1.3544   

               develop    countri    nation      sustain     model  \
pandem        0.857162    0.78996  0.959555     0.802517   1.27776   
polici        0.282271  0.0978683  0.502134     0.059464    0.9948   
health        0.772437   0.731875  0.801066     0.735623   1.23484   
covid          1.02832   0.997722   1.10818      0.99013   1.40107   
impact        0.288594   0.176283  0.484074     0.151684   1.00463   
2020          0.977722   0.935336   1.06107     0.927968   1.35636   
develop    5.26836e-09   0.255258  0.569821     0.280661   1.02858   
countri       0.255258          0  0.510813     0.104083  0.999265   
nation        0.569821   0.510813         0     0.493141    1.1093   
sustain       0.280661   0.104083  0.493141  2.32831e-10  0.991911   
model          1.02858   0.999265    1.1093     0.991911         0   
imag           1.02321   0.996246   1.10552     0.988782   1.39962   
use            1.03463          1   1.11456     0.992875    1.4032   
detect        0.974076   0.955596   1.08787     0.957287   1.37647   
data            1.0127   0.978576    1.0973     0.975925   1.39042   
class          1.02858   0.988146   1.09818     0.984182   1.39803   
pneumonia     0.989492   0.948285   1.05844     0.943301   1.36889   
learn          1.00933   0.978394   1.10088     0.970817   1.38704   
dataset        1.01931   0.990649   1.09507     0.985643   1.39858   

                  imag       use    detect         data     class pneumonia  \
pandem         1.28264   1.27322   1.25514      1.27319    1.2708   1.26171   
polici        0.990826  0.991493  0.961398     0.977506  0.986404  0.944239   
health         1.23416   1.23268   1.19357      1.21628   1.22612   1.19465   
covid            1.398   1.40142   1.36915      1.38877   1.39319    1.3651   
impact         1.00471   1.00621  0.959384     0.979238  0.993789  0.962037   
2020           1.35426   1.35885   1.33614      1.35008    1.3532   1.32371   
develop        1.02321   1.03463  0.974076       1.0127   1.02858  0.989492   
countri       0.996246         1  0.955596     0.978576  0.988146  0.948285   
nation         1.10552   1.11456   1.08787       1.0973   1.09818   1.05844   
sustain       0.988782  0.992875  0.957287     0.975925  0.984182  0.943301   
model          1.39962    1.4032   1.37647      1.39042   1.39803   1.36889   
imag       1.49012e-08   1.40125    1.3768      1.39395   1.39545   1.36552   
use            1.40125         0   1.37741      1.39107   1.39848   1.36661   
detect          1.3768   1.37741         0      1.36941   1.37578   1.33973   
data           1.39395   1.39107   1.36941  2.98023e-08   1.38623   1.35703   
class          1.39545   1.39848   1.37578      1.38623         0   1.35644   
pneumonia      1.36552   1.36661   1.33973      1.35703   1.35644         0   
learn            1.385   1.38923   1.36418      1.37588   1.38105   1.35173   
dataset        1.39747   1.39759   1.37376       1.3903   1.39314   1.36415   

              learn   dataset  
pandem      1.25492   1.27099  
polici     0.971011  0.987866  
health       1.2104   1.23441  
covid       1.38827   1.39641  
impact     0.961697  0.998986  
2020        1.34298    1.3544  
develop     1.00933   1.01931  
countri    0.978394  0.990649  
nation      1.10088   1.09507  
sustain    0.970817  0.985643  
model       1.38704   1.39858  
imag          1.385   1.39747  
use         1.38923   1.39759  
detect      1.36418   1.37376  
data        1.37588    1.3903  
class       1.38105   1.39314  
pneumonia   1.35173   1.36415  
learn             0    1.3838  
dataset      1.3838         0  
Pairwise-distance
             pandem    polici    health        covid       impact      2020  \
pandem            0   1.17558   1.37708      1.39721       1.3847   1.39367   
polici      1.17558         0    1.1391      1.18201      1.17001   1.17658   
health      1.37708    1.1391         0      1.38173       1.3707   1.37785   
covid       1.39721   1.18201   1.38173  2.10734e-08      1.39031   1.40014   
impact       1.3847   1.17001    1.3707      1.39031  2.98023e-08    1.3918   
2020        1.39367   1.17658   1.37785      1.40014       1.3918         0   
develop     1.36887   1.16734   1.34654      1.37228      1.36486   1.37052   
countri     1.36715   1.14751   1.34977      1.37263      1.36417   1.37329   
nation      1.25795  0.942172   1.24957      1.26425      1.25484   1.25943   
sustain     1.34558   1.13509   1.32896      1.35597      1.34091   1.35162   
model       1.01878  0.759153   1.01277      1.04557      1.03056   1.03765   
imag       0.987712  0.639016  0.960914     0.994026     0.982477  0.990005   
use         1.27861   1.04098   1.26281      1.29385      1.27682   1.28617   
detect     0.984703    0.6405  0.960757     0.993522     0.980479  0.989221   
data         1.3231   1.10348   1.29412      1.32706      1.32112   1.32923   
class      0.985077  0.638388  0.960203     0.992148     0.980061  0.985688   
pneumonia  0.985547  0.641605  0.961031     0.992594     0.981821  0.989054   
learn       1.00621   0.64735  0.992401      1.00856       1.0102   1.00907   
dataset    0.984247  0.638738  0.959281     0.992579     0.979842  0.988415   

            develop   countri       nation   sustain        model        imag  \
pandem      1.36887   1.36715      1.25795   1.34558      1.01878    0.987712   
polici      1.16734   1.14751     0.942172   1.13509     0.759153    0.639016   
health      1.34654   1.34977      1.24957   1.32896      1.01277    0.960914   
covid       1.37228   1.37263      1.26425   1.35597      1.04557    0.994026   
impact      1.36486   1.36417      1.25484   1.34091      1.03056    0.982477   
2020        1.37052   1.37329      1.25943   1.35162      1.03765    0.990005   
develop           0   1.32649      1.17097   1.28754      1.01364    0.946053   
countri     1.32649         0      1.24269   1.33141      0.99738    0.952918   
nation      1.17097   1.24269  1.49012e-08   1.20892     0.825985    0.785984   
sustain     1.28754   1.33141      1.20892         0     0.961421    0.929365   
model       1.01364   0.99738     0.825985  0.961421  5.26836e-09    0.314507   
imag       0.946053  0.952918     0.785984  0.929365     0.314507  1.6131e-09   
use         1.25653   1.27391       1.1299   1.22557     0.776152    0.820354   
detect      0.94742   0.95023     0.783767  0.924051     0.314485   0.0686742   
data        1.29767   1.30339      1.19095   1.27465     0.967205    0.890903   
class      0.947802  0.950137     0.781917  0.924266     0.314651    0.062147   
pneumonia  0.949065   0.95166      0.78507  0.923673      0.31063   0.0664119   
learn      0.982492  0.975923     0.822156  0.933701     0.375298    0.224652   
dataset    0.946579  0.949827     0.782375  0.925001     0.315564   0.0621299   

                use       detect      data      class  pneumonia        learn  \
pandem      1.27861     0.984703    1.3231   0.985077   0.985547      1.00621   
polici      1.04098       0.6405   1.10348   0.638388   0.641605      0.64735   
health      1.26281     0.960757   1.29412   0.960203   0.961031     0.992401   
covid       1.29385     0.993522   1.32706   0.992148   0.992594      1.00856   
impact      1.27682     0.980479   1.32112   0.980061   0.981821       1.0102   
2020        1.28617     0.989221   1.32923   0.985688   0.989054      1.00907   
develop     1.25653      0.94742   1.29767   0.947802   0.949065     0.982492   
countri     1.27391      0.95023   1.30339   0.950137    0.95166     0.975923   
nation       1.1299     0.783767   1.19095   0.781917    0.78507     0.822156   
sustain     1.22557     0.924051   1.27465   0.924266   0.923673     0.933701   
model      0.776152     0.314485  0.967205   0.314651    0.31063     0.375298   
imag       0.820354    0.0686742  0.890903   0.062147  0.0664119     0.224652   
use               0     0.819518   1.17363   0.821355   0.822347     0.824807   
detect     0.819518  6.58545e-10  0.871011   0.045286  0.0554104     0.210866   
data        1.17363     0.871011         0   0.889675   0.889525     0.917891   
class      0.821355     0.045286  0.889675          0  0.0402918     0.204918   
pneumonia  0.822347    0.0554104  0.889525  0.0402918          0     0.203486   
learn      0.824807     0.210866  0.917891   0.204918   0.203486  7.45058e-09   
dataset    0.821047    0.0361035   0.86952  0.0287117  0.0420851     0.205585   

             dataset  
pandem      0.984247  
polici      0.638738  
health      0.959281  
covid       0.992579  
impact      0.979842  
2020        0.988415  
develop     0.946579  
countri     0.949827  
nation      0.782375  
sustain     0.925001  
model       0.315564  
imag       0.0621299  
use         0.821047  
detect     0.0361035  
data         0.86952  
class      0.0287117  
pneumonia  0.0420851  
learn       0.205585  
dataset            0

(-0.46950690647912063, 0.98796, -2.1198161571850607)


mantel_test_n(5)

Pairwise-distance
              trial       vaccin        stage         late       expect  \
trial   2.32831e-10    0.0321631     0.116023    0.0315024     0.106246   
vaccin    0.0321631  6.58545e-10     0.122716    0.0424287     0.113222   
stage      0.116023     0.122716  1.86265e-09      0.11955     0.154401   
late      0.0315024    0.0424287      0.11955  4.65661e-10     0.114867   
expect     0.106246     0.113222     0.154401     0.114867  3.72529e-09   
covid      0.990182     0.990235     0.992167     0.992115     0.993154   
model      0.991998     0.992868     0.999485     0.992032     0.994909   
imag       0.988698     0.989315      0.99235     0.989705     0.999658   
use        0.992879     0.994046     0.993865     0.993597     0.998827   
detect     0.957615     0.950483     0.968579     0.957712     0.970974   

           covid     model         imag       use    detect  
trial   0.990182  0.991998     0.988698  0.992879  0.957615  
vaccin  0.990235  0.992868     0.989315  0.994046  0.950483  
stage   0.992167  0.999485      0.99235  0.993865  0.968579  
late    0.992115  0.992032     0.989705  0.993597  0.957712  
expect  0.993154  0.994909     0.999658  0.998827  0.970974  
covid          0   1.40107        1.398   1.40142   1.36915  
model    1.40107         0      1.39962    1.4032   1.37647  
imag       1.398   1.39962  1.49012e-08   1.40125    1.3768  
use      1.40142    1.4032      1.40125         0   1.37741  
detect   1.36915   1.37647       1.3768   1.37741         0  
Pairwise-distance
           trial       vaccin     stage      late    expect     covid  \
trial          0      0.89187  0.285876  0.369334  0.342783    1.0699   
vaccin   0.89187  1.49012e-08  0.804654  0.817278  0.791699     1.192   
stage   0.285876     0.804654         0  0.199184  0.132632  0.982254   
late    0.369334     0.817278  0.199184         0  0.217407  0.985856   
expect  0.342783     0.791699  0.132632  0.217407         0  0.960874   
covid     1.0699        1.192  0.982254  0.985856  0.960874         0   
model   0.573892     0.792777  0.398873  0.408372  0.321224  0.951933   
imag           0            0         0         0         0         0   
use     0.776119     0.984243  0.670821  0.669248  0.634805   1.07779   
detect  0.576555     0.832899  0.413428  0.418411   0.34069  0.940779   

              model imag          use    detect  
trial      0.573892    0     0.776119  0.576555  
vaccin     0.792777    0     0.984243  0.832899  
stage      0.398873    0     0.670821  0.413428  
late       0.408372    0     0.669248  0.418411  
expect     0.321224    0     0.634805   0.34069  
covid      0.951933    0      1.07779  0.940779  
model   7.45058e-09    0     0.593823  0.296615  
imag              0    0            0         0  
use        0.593823    0  1.49012e-08   0.59782  
detect     0.296615    0      0.59782         0

(-0.00835086488115451, 0.52709, -0.02417688171174474)


mantel_test_n(10)

Pairwise-distance
                 trial       vaccin        stage         late       expect  \
trial      2.32831e-10    0.0321631     0.116023    0.0315024     0.106246   
vaccin       0.0321631  6.58545e-10     0.122716    0.0424287     0.113222   
stage         0.116023     0.122716  1.86265e-09      0.11955     0.154401   
late         0.0315024    0.0424287      0.11955  4.65661e-10     0.114867   
expect        0.106246     0.113222     0.154401     0.114867  3.72529e-09   
underway             0            0            0            0            0   
dose                 0            0            0            0            0   
novemb       0.0510105    0.0598232     0.128761    0.0526716     0.123767   
addit         0.129501     0.130605     0.177833     0.128789     0.151949   
develop       0.275278     0.264194     0.308447     0.274625     0.294212   
covid         0.990182     0.990235     0.992167     0.992115     0.993154   
model         0.991998     0.992868     0.999485     0.992032     0.994909   
imag          0.988698     0.989315      0.99235     0.989705     0.999658   
use           0.992879     0.994046     0.993865     0.993597     0.998827   
detect        0.957615     0.950483     0.968579     0.957712     0.970974   
data          0.976038     0.976569     0.976525     0.976251     0.982452   
class         0.984029     0.984781     0.998048     0.985503     0.992714   
pneumonia     0.943055     0.944579       0.9582     0.936492     0.952281   
learn         0.971018     0.971987      0.96544     0.972029     0.979828   
dataset       0.985513     0.986322     0.997561     0.985488     0.992091   

          underway dose     novemb     addit      develop     covid     model  \
trial            0    0  0.0510105  0.129501     0.275278  0.990182  0.991998   
vaccin           0    0  0.0598232  0.130605     0.264194  0.990235  0.992868   
stage            0    0   0.128761  0.177833     0.308447  0.992167  0.999485   
late             0    0  0.0526716  0.128789     0.274625  0.992115  0.992032   
expect           0    0   0.123767  0.151949     0.294212  0.993154  0.994909   
underway         0    0          0         0            0         0         0   
dose             0    0          0         0            0         0         0   
novemb           0    0          0   0.14615     0.280098  0.992299  0.992432   
addit            0    0    0.14615         0     0.274563  0.998074   1.00316   
develop          0    0   0.280098  0.274563  5.26836e-09   1.02832   1.02858   
covid            0    0   0.992299  0.998074      1.02832         0   1.40107   
model            0    0   0.992432   1.00316      1.02858   1.40107         0   
imag             0    0     0.9894   1.00315      1.02321     1.398   1.39962   
use              0    0   0.993779   1.00404      1.03463   1.40142    1.4032   
detect           0    0   0.963291  0.980799     0.974076   1.36915   1.37647   
data             0    0   0.977372  0.985247       1.0127   1.38877   1.39042   
class            0    0    0.98581  0.991452      1.02858   1.39319   1.39803   
pneumonia        0    0   0.943921  0.952584     0.989492    1.3651   1.36889   
learn            0    0   0.971609  0.986373      1.00933   1.38827   1.38704   
dataset          0    0   0.986215   0.99714      1.01931   1.39641   1.39858   

                  imag       use    detect         data     class pneumonia  \
trial         0.988698  0.992879  0.957615     0.976038  0.984029  0.943055   
vaccin        0.989315  0.994046  0.950483     0.976569  0.984781  0.944579   
stage          0.99235  0.993865  0.968579     0.976525  0.998048    0.9582   
late          0.989705  0.993597  0.957712     0.976251  0.985503  0.936492   
expect        0.999658  0.998827  0.970974     0.982452  0.992714  0.952281   
underway             0         0         0            0         0         0   
dose                 0         0         0            0         0         0   
novemb          0.9894  0.993779  0.963291     0.977372   0.98581  0.943921   
addit          1.00315   1.00404  0.980799     0.985247  0.991452  0.952584   
develop        1.02321   1.03463  0.974076       1.0127   1.02858  0.989492   
covid            1.398   1.40142   1.36915      1.38877   1.39319    1.3651   
model          1.39962    1.4032   1.37647      1.39042   1.39803   1.36889   
imag       1.49012e-08   1.40125    1.3768      1.39395   1.39545   1.36552   
use            1.40125         0   1.37741      1.39107   1.39848   1.36661   
detect          1.3768   1.37741         0      1.36941   1.37578   1.33973   
data           1.39395   1.39107   1.36941  2.98023e-08   1.38623   1.35703   
class          1.39545   1.39848   1.37578      1.38623         0   1.35644   
pneumonia      1.36552   1.36661   1.33973      1.35703   1.35644         0   
learn            1.385   1.38923   1.36418      1.37588   1.38105   1.35173   
dataset        1.39747   1.39759   1.37376       1.3903   1.39314   1.36415   

              learn   dataset  
trial      0.971018  0.985513  
vaccin     0.971987  0.986322  
stage       0.96544  0.997561  
late       0.972029  0.985488  
expect     0.979828  0.992091  
underway          0         0  
dose              0         0  
novemb     0.971609  0.986215  
addit      0.986373   0.99714  
develop     1.00933   1.01931  
covid       1.38827   1.39641  
model       1.38704   1.39858  
imag          1.385   1.39747  
use         1.38923   1.39759  
detect      1.36418   1.37376  
data        1.37588    1.3903  
class       1.38105   1.39314  
pneumonia   1.35173   1.36415  
learn             0    1.3838  
dataset      1.3838         0  
Pairwise-distance
              trial       vaccin     stage      late     expect  underway  \
trial             0      0.89187  0.285876  0.369334   0.342783  0.429897   
vaccin      0.89187  1.49012e-08  0.804654  0.817278   0.791699  0.821063   
stage      0.285876     0.804654         0  0.199184   0.132632  0.258394   
late       0.369334     0.817278  0.199184         0   0.217407  0.302201   
expect     0.342783     0.791699  0.132632  0.217407          0  0.232943   
underway   0.429897     0.821063  0.258394  0.302201   0.232943         0   
dose       0.360355     0.781183  0.138436  0.212674  0.0923247  0.212702   
novemb     0.547999     0.857129  0.412123  0.435093   0.388714  0.427285   
addit      0.449831     0.814683  0.295908  0.328898   0.256565  0.315661   
develop    0.704357     0.965487  0.676245  0.689742   0.649834  0.664629   
covid        1.0699        1.192  0.982254  0.985856   0.960874  0.962364   
model      0.573892     0.792777  0.398873  0.408372   0.321224  0.357396   
imag              0            0         0         0          0         0   
use        0.776119     0.984243  0.670821  0.669248   0.634805  0.634562   
detect     0.576555     0.832899  0.413428  0.418411    0.34069  0.207654   
data       0.609664     0.858408  0.473724   0.49565   0.441946  0.464386   
class             0            0         0         0          0         0   
pneumonia  0.602326     0.848442   0.44677  0.457108   0.366837  0.392718   
learn             0            0         0         0          0         0   
dataset           0            0         0         0          0         0   

                  dose    novemb     addit      develop     covid  \
trial         0.360355  0.547999  0.449831     0.704357    1.0699   
vaccin        0.781183  0.857129  0.814683     0.965487     1.192   
stage         0.138436  0.412123  0.295908     0.676245  0.982254   
late          0.212674  0.435093  0.328898     0.689742  0.985856   
expect       0.0923247  0.388714  0.256565     0.649834  0.960874   
underway      0.212702  0.427285  0.315661     0.664629  0.962364   
dose       6.45239e-09  0.368458  0.234741     0.627806  0.941894   
novemb        0.368458         0  0.431946     0.727097   1.00184   
addit         0.234741  0.431946         0     0.664635  0.952159   
develop       0.627806  0.727097  0.664635  1.05367e-08   1.07081   
covid         0.941894   1.00184  0.952159      1.07081         0   
model         0.286925  0.446042  0.337936     0.650876  0.951933   
imag                 0         0         0            0         0   
use             0.6079  0.691562  0.636751     0.833904   1.07779   
detect        0.303156  0.454813  0.355475     0.649915  0.940779   
data          0.414799  0.545249  0.475833     0.738415   1.01369   
class                0         0         0            0         0   
pneumonia     0.348283  0.483969  0.398112     0.669283  0.960479   
learn                0         0         0            0         0   
dataset              0         0         0            0         0   

                 model imag          use    detect      data class pneumonia  \
trial         0.573892    0     0.776119  0.576555  0.609664     0  0.602326   
vaccin        0.792777    0     0.984243  0.832899  0.858408     0  0.848442   
stage         0.398873    0     0.670821  0.413428  0.473724     0   0.44677   
late          0.408372    0     0.669248  0.418411   0.49565     0  0.457108   
expect        0.321224    0     0.634805   0.34069  0.441946     0  0.366837   
underway      0.357396    0     0.634562  0.207654  0.464386     0  0.392718   
dose          0.286925    0       0.6079  0.303156  0.414799     0  0.348283   
novemb        0.446042    0     0.691562  0.454813  0.545249     0  0.483969   
addit         0.337936    0     0.636751  0.355475  0.475833     0  0.398112   
develop       0.650876    0     0.833904  0.649915  0.738415     0  0.669283   
covid         0.951933    0      1.07779  0.940779   1.01369     0  0.960479   
model      7.45058e-09    0     0.593823  0.296615  0.450407     0  0.346303   
imag                 0    0            0         0         0     0         0   
use           0.593823    0  1.49012e-08   0.59782  0.579482     0  0.636898   
detect        0.296615    0      0.59782         0  0.461009     0  0.334936   
data          0.450407    0     0.579482  0.461009         0     0  0.497274   
class                0    0            0         0         0     0         0   
pneumonia     0.346303    0     0.636898  0.334936  0.497274     0         0   
learn                0    0            0         0         0     0         0   
dataset              0    0            0         0         0     0         0   

          learn dataset  
trial         0       0  
vaccin        0       0  
stage         0       0  
late          0       0  
expect        0       0  
underway      0       0  
dose          0       0  
novemb        0       0  
addit         0       0  
develop       0       0  
covid         0       0  
model         0       0  
imag          0       0  
use           0       0  
detect        0       0  
data          0       0  
class         0       0  
pneumonia     0       0  
learn         0       0  
dataset       0       0

(-0.16970638044077413, 0.79436, -0.8424665192300715)

Experiment 2: Document at various level for small dataset(COVID-science:non-science)¶

1. Article level¶

LSA on covid-science-small.txt at article level¶

LSA on covid-non-science-small.txt at article level¶

LSA on covid-psychology-small.txt at article level¶

2. 2-3 paragraphs¶

LSA on covid-science-small.txt at 2-3 paragraph level¶

LSA on covid-non-science-small.txt at 2-3 paragraph level¶

LSA on covid-psychology-small.txt at 2-3 paragraph level¶

Paragraph level¶

LSA oncovid-science-small.txt at paragraph level¶

LSA on covid-non-science-small.txt at paragraph level¶

LSA on covid-psychology-small.txt at paragraph level¶